text stringlengths 306 1.51k |
|---|
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Bitdefender’s forensics and investigation team was contacted to look into a security incident that started in May 2018 with an email received by two of the bank’s employees .' [/INST] [Bitdefender’s:B-HackOrg, forensics:O, and:O, investigation:O, team:O, was:O, contacted:O, to:O, look:O, into:O, a:O, security:O, incident:O, that:O, started:O, in:O, May:B-Time, 2018:I-Time, with:O, an:O, email:O, received:O, by:O, two:O, of:O, the:O, bank’s:B-Idus, employees:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The Carbanak group , which has a long track record of compromising infrastructure belonging to financial institutions , is still active .' [/INST] [The:O, Carbanak:B-HackOrg, group:O, ,:O, which:O, has:O, a:O, long:O, track:O, record:O, of:O, compromising:O, infrastructure:O, belonging:O, to:O, financial:B-Idus, institutions:O, ,:O, is:O, still:O, active:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Its purpose remains to manipulate financial assets , such as transferring funds from bank accounts or taking over ATM infrastructures and instructing them to dispense cash at predetermined time intervals .' [/INST] [Its:B-HackOrg, purpose:O, remains:O, to:O, manipulate:O, financial:B-Way, assets:O, ,:O, such:O, as:O, transferring:O, funds:O, from:O, bank:B-Idus, accounts:O, or:O, taking:O, over:O, ATM:O, infrastructures:O, and:O, instructing:O, them:O, to:O, dispense:O, cash:O, at:O, predetermined:O, time:O, intervals:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'If the attack had succeeded , it would have given hackers control over the ATM network , while money mules would have been standing by the ATM machines at pre-set time intervals to cash them out .' [/INST] [If:O, the:O, attack:O, had:O, succeeded:O, ,:O, it:O, would:O, have:O, given:O, hackers:B-HackOrg, control:B-Purp, over:I-Purp, the:O, ATM:O, network:O, ,:O, while:O, money:O, mules:O, would:O, have:O, been:O, standing:O, by:O, the:O, ATM:O, machines:O, at:O, pre-set:O, time:O, intervals:O, to:O, cash:O, them:O, out:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The actors uploaded a variety of tools that they used to perform additional activities on the compromised network , such as dumping credentials , as well as locating and pivoting to additional systems on the network .' [/INST] [The:O, actors:B-HackOrg, uploaded:O, a:O, variety:O, of:O, tools:O, that:O, they:O, used:O, to:O, perform:O, additional:O, activities:O, on:O, the:O, compromised:O, network:O, ,:O, such:O, as:O, dumping:B-Tool, credentials:I-Tool, ,:O, as:O, well:O, as:O, locating:B-Way, and:O, pivoting:B-Way, to:O, additional:O, systems:O, on:O, the:O, network:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We believe Emissary Panda exploited a recently patched vulnerability in Microsoft SharePoint tracked by CVE-2019-0604 , which is a remote code execution vulnerability used to compromise the server and eventually install a webshell .' [/INST] [We:O, believe:O, Emissary:B-HackOrg, Panda:I-HackOrg, exploited:O, a:O, recently:O, patched:O, vulnerability:B-Exp, in:O, Microsoft:O, SharePoint:O, tracked:O, by:O, CVE-2019-0604:B-Exp, ,:O, which:O, is:O, a:O, remote:O, code:O, execution:O, vulnerability:O, used:O, to:O, compromise:O, the:O, server:O, and:O, eventually:O, install:O, a:O, webshell:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Bitdefender’s investigation shows the attackers’ main methods remain to quietly infiltrate the infrastructure by establishing a foothold on an employee’s system , then move laterally across the infrastructure or elevate privileges to find critical systems that manage financial transactions or ATM networks .' [/INST] [Bitdefender’s:B-HackOrg, investigation:O, shows:O, the:O, attackers’:O, main:O, methods:O, remain:O, to:O, quietly:O, infiltrate:O, the:O, infrastructure:O, by:O, establishing:B-Way, a:O, foothold:O, on:O, an:O, employee’s:O, system:O, ,:O, then:O, move:O, laterally:O, across:O, the:O, infrastructure:O, or:O, elevate:O, privileges:O, to:O, find:O, critical:O, systems:O, that:O, manage:O, financial:B-Idus, transactions:I-Idus, or:O, ATM:B-Idus, networks:I-Idus, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We also found the China Chopper webshell on the SharePoint servers , which has also been used by the Emissary Panda threat group .' [/INST] [We:O, also:O, found:O, the:O, China:B-Tool, Chopper:I-Tool, webshell:I-Tool, on:O, the:O, SharePoint:O, servers:O, ,:O, which:O, has:O, also:O, been:O, used:O, by:O, the:O, Emissary:B-HackOrg, Panda:I-HackOrg, threat:O, group:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Of particular note is their use of tools to identify systems vulnerable to CVE-2017-0144 , which is the same vulnerability exploited by EternalBlue that is best known for its use in the WannaCry attacks of 2017 .' [/INST] [Of:O, particular:O, note:O, is:O, their:O, use:O, of:O, tools:O, to:O, identify:O, systems:O, vulnerable:O, to:O, CVE-2017-0144:B-Exp, ,:O, which:O, is:O, the:O, same:O, vulnerability:O, exploited:O, by:O, EternalBlue:B-OffAct, that:O, is:O, best:O, known:O, for:O, its:O, use:O, in:O, the:O, WannaCry:B-OffAct, attacks:I-OffAct, of:O, 2017:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In addition to the aforementioned post-exploitation tools , the actors used these webshells to upload legitimate executables that they would use DLL sideloading to run a malicious DLL that has code overlaps with known Emissary Panda attacks .' [/INST] [In:O, addition:O, to:O, the:O, aforementioned:O, post-exploitation:O, tools:O, ,:O, the:O, actors:O, used:O, these:O, webshells:O, to:O, upload:O, legitimate:O, executables:O, that:O, they:O, would:O, use:O, DLL:O, sideloading:O, to:O, run:B-Way, a:I-Way, malicious:I-Way, DLL:I-Way, that:O, has:O, code:O, overlaps:O, with:O, known:O, Emissary:B-HackOrg, Panda:I-HackOrg, attacks:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This webshell activity took place across three SharePoint servers hosted by two different government organizations between April 1 , 2019 and April 16 , 2019 , where actors uploaded a total of 24 unique executables across the three SharePoint servers .' [/INST] [This:O, webshell:B-OffAct, activity:I-OffAct, took:O, place:O, across:O, three:O, SharePoint:O, servers:O, hosted:O, by:O, two:O, different:O, government:O, organizations:O, between:O, April:B-Time, 1:I-Time, ,:O, 2019:B-Time, and:O, April:B-Time, 16:I-Time, ,:O, 2019:B-Time, ,:O, where:O, actors:O, uploaded:O, a:O, total:O, of:O, 24:O, unique:O, executables:O, across:O, the:O, three:O, SharePoint:O, servers:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The timeline shows three main clusters of activity across the three webshells , with activity occurring on two separate webshells (green and orange) within a very small window of time on April 2 , 2019 and the activity involving the third webshell two weeks later on April 16 , 2019 .' [/INST] [The:O, timeline:O, shows:O, three:O, main:O, clusters:O, of:O, activity:B-OffAct, across:O, the:O, three:O, webshells:O, ,:O, with:O, activity:O, occurring:O, on:O, two:O, separate:O, webshells:O, (green:O, and:O, orange):O, within:O, a:O, very:O, small:O, window:O, of:O, time:O, on:O, April:B-Time, 2:I-Time, ,:O, 2019:B-Time, and:O, the:O, activity:O, involving:O, the:O, third:O, webshell:O, two:O, weeks:O, later:O, on:O, April:B-Time, 16:I-Time, ,:O, 2019:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In April 2019 , several national security organizations released alerts on CVE-2019-0604 exploitation , including the Saudi Arabian National Cyber Security Center and the Canadian Center for Cyber Security .' [/INST] [In:O, April:B-Time, 2019:I-Time, ,:O, several:O, national:O, security:O, organizations:O, released:O, alerts:O, on:O, CVE-2019-0604:B-Exp, exploitation:O, ,:O, including:O, the:O, Saudi:B-Area, Arabian:I-Area, National:O, Cyber:B-SecTeam, Security:I-SecTeam, Center:I-SecTeam, and:O, the:O, Canadian:B-SecTeam, Center:I-SecTeam, for:O, Cyber:O, Security:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Based on the functionality of the various tools uploaded to the webshells , we believe the threat actors breach the SharePoint servers to use as a beachhead , then attempt to move laterally across the network via stolen credentials and exploiting vulnerabilities .' [/INST] [Based:O, on:O, the:O, functionality:O, of:O, the:O, various:O, tools:O, uploaded:O, to:O, the:O, webshells:O, ,:O, we:O, believe:O, the:O, threat:B-HackOrg, actors:I-HackOrg, breach:O, the:O, SharePoint:O, servers:O, to:O, use:O, as:O, a:O, beachhead:O, ,:O, then:O, attempt:O, to:O, move:B-Purp, laterally:O, across:O, the:O, network:O, via:O, stolen:B-Purp, credentials:I-Purp, and:O, exploiting:B-Purp, vulnerabilities:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We also observed the actors uploading custom backdoors such as HyperBro which is commonly associated with Emissary Panda .' [/INST] [We:O, also:O, observed:O, the:O, actors:B-HackOrg, uploading:O, custom:O, backdoors:O, such:O, as:O, HyperBro:B-Tool, which:O, is:O, commonly:O, associated:O, with:O, Emissary:B-HackOrg, Panda:I-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Both of these alerts discussed campaigns in which actors used the CVE-2019-0604 to exploit SharePoint servers to install the China Chopper webshell .' [/INST] [Both:O, of:O, these:O, alerts:O, discussed:O, campaigns:O, in:O, which:O, actors:B-HackOrg, used:O, the:O, CVE-2019-0604:B-Exp, to:O, exploit:O, SharePoint:O, servers:O, to:O, install:O, the:O, China:B-Tool, Chopper:I-Tool, webshell:I-Tool, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'During our research into this attack campaign , Unit 42 gathered several tools that the Emissary Panda uploaded to the three webshells at the two government organizations .' [/INST] [During:O, our:O, research:O, into:O, this:O, attack:O, campaign:O, ,:O, Unit:B-SecTeam, 42:I-SecTeam, gathered:O, several:O, tools:O, that:O, the:O, Emissary:B-HackOrg, Panda:I-HackOrg, uploaded:O, to:O, the:O, three:O, webshells:O, at:O, the:O, two:O, government:B-Org, organizations:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We also observed the actors uploading the HyperBro backdoor to one of the webshells , as well as legitimate executables that would sideload malicious DLLs that have overlapping code associated with known Emissary Panda activity .' [/INST] [We:O, also:O, observed:O, the:O, actors:B-HackOrg, uploading:O, the:O, HyperBro:B-Tool, backdoor:I-Tool, to:O, one:O, of:O, the:O, webshells:O, ,:O, as:O, well:O, as:O, legitimate:O, executables:O, that:O, would:O, sideload:B-Way, malicious:I-Way, DLLs:B-Way, that:O, have:O, overlapping:O, code:O, associated:O, with:O, known:O, Emissary:B-HackOrg, Panda:I-HackOrg, activity:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Lastly , we saw the actor uploading a custom backdoor called HyperBro , which has been associated with Emissary Panda operations in the past .' [/INST] [Lastly:O, ,:O, we:O, saw:O, the:O, actor:B-HackOrg, uploading:O, a:O, custom:O, backdoor:O, called:O, HyperBro:B-Tool, ,:O, which:O, has:O, been:O, associated:O, with:O, Emissary:B-HackOrg, Panda:I-HackOrg, operations:O, in:O, the:O, past:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The other overlapping files are tools used by the adversary to locate other systems on the network (etool.exe) , check to see if they are vulnerable to CVE-2017-0144 (EternalBlue) patched in MS07-010 (checker1.exe) and pivot to them using remote execution functionality offered by a tool similar to PsExec offered by Impacket (psexec.exe) .' [/INST] [The:O, other:O, overlapping:O, files:O, are:O, tools:O, used:O, by:O, the:O, adversary:O, to:O, locate:O, other:O, systems:O, on:O, the:O, network:O, (etool.exe):O, ,:O, check:O, to:O, see:O, if:O, they:O, are:O, vulnerable:O, to:O, CVE-2017-0144:B-Exp, (EternalBlue):O, patched:O, in:O, MS07-010:B-SamFile, (checker1.exe):O, and:O, pivot:O, to:O, them:O, using:O, remote:O, execution:O, functionality:O, offered:O, by:O, a:O, tool:O, similar:O, to:O, PsExec:B-SamFile, offered:O, by:O, Impacket:O, (psexec.exe):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Also , the NCSC advisory mentioned that the actors used a file name stylecss.aspx for their webshell , which is the same filename we saw associated with China Chopper .' [/INST] [Also:O, ,:O, the:O, NCSC:O, advisory:O, mentioned:O, that:O, the:O, actors:O, used:O, a:O, file:O, name:O, stylecss.aspx:B-SamFile, for:O, their:O, webshell:O, ,:O, which:O, is:O, the:O, same:O, filename:O, we:O, saw:O, associated:O, with:O, China:B-SamFile, Chopper:I-SamFile, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We will provide an analysis of the HyperBro tool in an upcoming section .' [/INST] [We:B-SecTeam, will:O, provide:O, an:O, analysis:O, of:O, the:O, HyperBro:B-SamFile, tool:O, in:O, an:O, upcoming:O, section:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'However , using NCC Group’s research published in May 2018 , we were able to discover code overlaps between these DLLs and a sideloaded DLL that ran the SysUpdate tool that the NCC group has associated with an Emissary Panda campaign .' [/INST] [However:O, ,:O, using:O, NCC:B-SecTeam, Group’s:O, research:O, published:O, in:O, May:O, 2018:O, ,:O, we:O, were:O, able:O, to:O, discover:O, code:O, overlaps:O, between:O, these:O, DLLs:O, and:O, a:O, sideloaded:O, DLL:O, that:O, ran:O, the:O, SysUpdate:O, tool:O, that:O, the:O, NCC:B-SecTeam, group:O, has:O, associated:O, with:O, an:O, Emissary:B-HackOrg, Panda:I-HackOrg, campaign:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The list also includes several hack tools , such as Mimikatz for credential dumping and several compiled python scripts used to locate and compromise other systems on the local network .' [/INST] [The:O, list:O, also:O, includes:O, several:O, hack:B-Tool, tools:I-Tool, ,:O, such:O, as:O, Mimikatz:B-Tool, for:O, credential:O, dumping:O, and:O, several:O, compiled:O, python:B-Tool, scripts:I-Tool, used:O, to:O, locate:O, and:O, compromise:O, other:O, systems:O, on:O, the:O, local:O, network:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Unfortunately , we do not have access to the PYTHON33.hlp or CreateTsMediaAdm.hlp files , so we do not know the final payload loaded by either of these DLLs .' [/INST] [Unfortunately:O, ,:O, we:O, do:O, not:O, have:O, access:O, to:O, the:O, PYTHON33.hlp:O, or:O, CreateTsMediaAdm.hlp:O, files:O, ,:O, so:O, we:O, do:O, not:O, know:O, the:O, final:O, payload:O, loaded:O, by:O, either:O, of:O, these:O, DLLs:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Figure 9 shows a code comparison between the PYTHON33.dll (right) and inicore_v2.3.30.dll (left) (SHA256: 4d65d371a789aabe1beadcc10b38da1f998cd3ec87d4cc1cfbf0af014b783822) , which was sideloaded to run the SysUpdate tool in a previous Emissary Panda campaign .' [/INST] [Figure:O, 9:O, shows:O, a:O, code:O, comparison:O, between:O, the:O, PYTHON33.dll:B-SamFile, (right):O, and:O, inicore_v2.3.30.dll:B-SamFile, (left):O, (SHA256::O, 4d65d371a789aabe1beadcc10b38da1f998cd3ec87d4cc1cfbf0af014b783822):O, ,:O, which:O, was:O, sideloaded:O, to:O, run:O, the:O, SysUpdate:B-Tool, tool:O, in:O, a:O, previous:O, Emissary:B-HackOrg, Panda:I-HackOrg, campaign:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The Emissary Panda threat group loaded the China Chopper webshell onto SharePoint servers at two Government organizations in the Middle East , which we believe with high confidence involved exploiting a remote code execution vulnerability in SharePoint tracked in CVE-2019-0604 .' [/INST] [The:O, Emissary:B-HackOrg, Panda:I-HackOrg, threat:O, group:O, loaded:O, the:O, China:B-Tool, Chopper:I-Tool, webshell:O, onto:O, SharePoint:O, servers:O, at:O, two:O, Government:O, organizations:O, in:O, the:O, Middle:B-Area, East:I-Area, ,:O, which:O, we:O, believe:O, with:O, high:O, confidence:O, involved:O, exploiting:O, a:O, remote:O, code:O, execution:O, vulnerability:O, in:O, SharePoint:O, tracked:O, in:O, CVE-2019-0604:B-Exp, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The files uploaded to this webshell included the same compiled python script that would scan remote systems that were vulnerable to CVE-2017-0144 (EternalBlue) that we saw uploaded to the other errr.aspx webshell .' [/INST] [The:O, files:O, uploaded:O, to:O, this:O, webshell:O, included:O, the:O, same:O, compiled:O, python:B-Tool, script:I-Tool, that:O, would:O, scan:O, remote:O, systems:O, that:O, were:O, vulnerable:O, to:O, CVE-2017-0144:B-Exp, (EternalBlue):O, that:O, we:O, saw:O, uploaded:O, to:O, the:O, other:O, errr.aspx:B-SamFile, webshell:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'According to Microsoft’s advisory , this vulnerability was patched on March 12 , 2019 and we first saw the webshell activity on April 1 , 2019 .' [/INST] [According:O, to:O, Microsoft’s:B-SecTeam, advisory:O, ,:O, this:O, vulnerability:O, was:O, patched:O, on:O, March:B-Time, 12:I-Time, ,:O, 2019:B-Time, and:O, we:O, first:O, saw:O, the:O, webshell:O, activity:O, on:O, April:B-Time, 1:I-Time, ,:O, 2019:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We believe the actors pivoted to other systems on the network using stolen credentials and by exploiting the CVE-2017-0144 (EternalBlue) vulnerability patched in MS17-010 .' [/INST] [We:O, believe:O, the:O, actors:B-HackOrg, pivoted:O, to:O, other:O, systems:O, on:O, the:O, network:O, using:O, stolen:B-OffAct, credentials:I-OffAct, and:O, by:O, exploiting:O, the:O, CVE-2017-0144:B-Exp, (EternalBlue):O, vulnerability:O, patched:O, in:O, MS17-010:B-SamFile, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Once the adversary established a foothold on the targeted network , they used China Chopper and other webshells to upload additional tools to the SharePoint server to dump credentials , perform network reconnaissance and pivot to other systems .' [/INST] [Once:O, the:O, adversary:O, established:O, a:O, foothold:O, on:O, the:O, targeted:O, network:O, ,:O, they:B-HackOrg, used:O, China:B-Tool, Chopper:I-Tool, and:O, other:O, webshells:O, to:O, upload:O, additional:O, tools:O, to:O, the:O, SharePoint:O, server:O, to:O, dump:B-Way, credentials:I-Way, ,:O, perform:O, network:O, reconnaissance:O, and:O, pivot:O, to:O, other:O, systems:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We also observed Emissary Panda uploading legitimate tools that would sideload DLLs , specifically the Sublime Text plugin host and the Microsoft’s Create Media application , both of which we had never seen used for DLL sideloading before .' [/INST] [We:O, also:O, observed:O, Emissary:B-HackOrg, Panda:I-HackOrg, uploading:O, legitimate:O, tools:O, that:O, would:O, sideload:B-Way, DLLs:I-Way, ,:O, specifically:O, the:O, Sublime:O, Text:O, plugin:O, host:O, and:O, the:O, Microsoft’s:O, Create:O, Media:O, application:O, ,:O, both:O, of:O, which:O, we:O, had:O, never:O, seen:O, used:O, for:O, DLL:B-Way, sideloading:I-Way, before:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Consequently , the Linux malware ecosystem is plagued by financial driven crypto-miners and DDoS botnet tools which mostly target vulnerable servers .' [/INST] [Consequently:O, ,:O, the:O, Linux:O, malware:O, ecosystem:O, is:O, plagued:O, by:O, financial:B-Idus, driven:O, crypto-miners:O, and:O, DDoS:O, botnet:O, tools:O, which:O, mostly:O, target:O, vulnerable:B-Org, servers:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We also observed the actors uploading legitimate tools that would sideload DLLs , specifically the Sublime Text plugin host and the Microsoft’s Create Media application , both of which we had never seen used for DLL sideloading before .' [/INST] [We:O, also:O, observed:O, the:O, actors:B-HackOrg, uploading:O, legitimate:O, tools:O, that:O, would:O, sideload:B-Way, DLLs:I-Way, ,:O, specifically:O, the:O, Sublime:B-Tool, Text:I-Tool, plugin:O, host:O, and:O, the:O, Microsoft’s:O, Create:O, Media:B-Tool, application:I-Tool, ,:O, both:O, of:O, which:O, we:O, had:O, never:O, seen:O, used:O, for:O, DLL:O, sideloading:O, before:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It has been active since at least 2013 , and has targeted individuals likely involved with the Ukrainian government .' [/INST] [It:B-HackOrg, has:O, been:O, active:O, since:O, at:O, least:O, 2013:B-Time, ,:O, and:O, has:O, targeted:O, individuals:O, likely:O, involved:O, with:O, the:O, Ukrainian:B-Area, government:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The group’s implants are characterized by the employment of information stealing tools among them being screenshot and document stealers delivered via a SFX , and made to achieve persistence through a scheduled task .' [/INST] [The:O, group’s:B-HackOrg, implants:O, are:O, characterized:O, by:O, the:O, employment:O, of:O, information:O, stealing:B-Tool, tools:I-Tool, among:O, them:O, being:O, screenshot:O, and:O, document:B-Tool, stealers:I-Tool, delivered:O, via:O, a:O, SFX:O, ,:O, and:O, made:O, to:O, achieve:O, persistence:O, through:O, a:O, scheduled:O, task:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The finding shows that EvilGnome operates on an IP address that was controlled by the Gamaredon group two months ago .' [/INST] [The:O, finding:O, shows:O, that:O, EvilGnome:B-Tool, operates:O, on:O, an:O, IP:O, address:O, that:O, was:O, controlled:O, by:O, the:O, Gamaredon:B-HackOrg, group:I-HackOrg, two:O, months:O, ago:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FIN7 operations are linked to numerous intrusion attempts having targeted hundreds of companies since at least as early as 2015 .' [/INST] [FIN7:B-HackOrg, operations:O, are:O, linked:O, to:O, numerous:O, intrusion:O, attempts:O, having:O, targeted:O, hundreds:O, of:O, companies:O, since:O, at:O, least:O, as:O, early:O, as:O, 2015:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The FIN7 intrusion set continued its tailored spear phishing campaigns throughout last year .' [/INST] [The:O, FIN7:B-HackOrg, intrusion:O, set:O, continued:O, its:O, tailored:O, spear:B-Way, phishing:I-Way, campaigns:O, throughout:O, last:B-Time, year:I-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In addition , during the investigation , we discovered certain similarities to other attacker groups that seemed to share or copy the FIN7 TTPs in their own operations .' [/INST] [In:O, addition:O, ,:O, during:O, the:O, investigation:O, ,:O, we:B-SecTeam, discovered:O, certain:O, similarities:O, to:O, other:O, attacker:B-HackOrg, groups:I-HackOrg, that:O, seemed:O, to:O, share:O, or:O, copy:O, the:O, FIN7:B-HackOrg, TTPs:O, in:O, their:O, own:O, operations:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In 2018-2019 , researchers of Kaspersky Lab’s Global Research and Analysis Team analyzed various campaigns that used the same Tactics Tools and Procedures (TTPs) as the historic FIN7 , leading the researchers to believe that this threat actor had remained active despite the 2018 arrests .' [/INST] [In:O, 2018-2019:B-Time, ,:O, researchers:O, of:O, Kaspersky:B-SecTeam, Lab’s:O, Global:O, Research:O, and:O, Analysis:O, Team:O, analyzed:O, various:B-OffAct, campaigns:I-OffAct, that:O, used:O, the:O, same:O, Tactics:O, Tools:O, and:O, Procedures:O, (TTPs):O, as:O, the:O, historic:O, FIN7:B-HackOrg, ,:O, leading:O, the:O, researchers:O, to:O, believe:O, that:O, this:O, threat:B-HackOrg, actor:I-HackOrg, had:O, remained:O, active:O, despite:O, the:O, 2018:O, arrests:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'One of the domains used by FIN7 in their 2018 campaign of spear phishing contained more than 130 email HackOrges , leading us to think that more than 130 companies had been targeted by the end of 2018 .' [/INST] [One:O, of:O, the:O, domains:O, used:O, by:O, FIN7:B-HackOrg, in:O, their:O, 2018:O, campaign:O, of:O, spear:B-Way, phishing:I-Way, contained:O, more:O, than:O, 130:O, email:O, HackOrges:O, ,:O, leading:O, us:O, to:O, think:O, that:O, more:O, than:O, 130:O, companies:O, had:O, been:O, targeted:O, by:O, the:O, end:O, of:O, 2018:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Interestingly , following some open-source publications about them , the FIN7 operators seems to have developed a homemade builder of malicious Office document using ideas from ThreadKit , which they employed during the summer of 2018 .' [/INST] [Interestingly:O, ,:O, following:O, some:O, open-source:O, publications:O, about:O, them:O, ,:O, the:O, FIN7:B-HackOrg, operators:O, seems:O, to:O, have:O, developed:O, a:O, homemade:O, builder:O, of:O, malicious:B-Tool, Office:I-Tool, document:I-Tool, using:O, ideas:O, from:O, ThreadKit:O, ,:O, which:O, they:O, employed:O, during:O, the:O, summer:O, of:O, 2018:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The first module downloaded by the GRIFFON malware to the victim’s computer is an information-gathering JScript , which allows the cybercriminals to understand the context of the infected workstation .' [/INST] [The:O, first:O, module:O, downloaded:O, by:O, the:O, GRIFFON:B-SamFile, malware:O, to:O, the:O, victim’s:O, computer:O, is:O, an:O, information-gathering:O, JScript:O, ,:O, which:O, allows:O, the:O, cybercriminals:O, to:O, understand:B-Features, the:O, context:O, of:O, the:O, infected:O, workstation:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The new GRIFFON implant is written to the hard drive before each execution , limiting the file-less” aspect of this method .' [/INST] [The:O, new:O, GRIFFON:B-SamFile, implant:O, is:O, written:B-Features, to:O, the:O, hard:O, drive:O, before:O, each:O, execution:O, ,:O, limiting:O, the:O, file-less”:O, aspect:O, of:O, this:O, method:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Given FIN7’s previous use of false security companies , we decided to look deeper into this one .' [/INST] [Given:O, FIN7’s:B-HackOrg, previous:O, use:O, of:O, false:O, security:B-Org, companies:I-Org, ,:O, we:O, decided:O, to:O, look:O, deeper:O, into:O, this:O, one:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This activity cluster , which Kaspersky Lab has followed for a few years , uses various implants for targeting mainly banks , and developers of banking and money processing software solutions .' [/INST] [This:O, activity:B-HackOrg, cluster:I-HackOrg, ,:O, which:O, Kaspersky:B-SecTeam, Lab:O, has:O, followed:O, for:O, a:O, few:O, years:O, ,:O, uses:O, various:O, implants:O, for:O, targeting:O, mainly:O, banks:B-Idus, ,:O, and:O, developers:O, of:O, banking:O, and:O, money:B-Idus, processing:I-Idus, software:O, solutions:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FIN7’s last campaigns were targeting banks in Europe and Central America .' [/INST] [FIN7’s:B-HackOrg, last:O, campaigns:O, were:O, targeting:O, banks:B-Idus, in:O, Europe:B-Area, and:O, Central:B-Area, America:I-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'After a successful penetration , FIN7 uses its own backdoors and the CobaltStrike framework or Powershell Empire components to hop to interesting parts of the network , where it can monetize its access .' [/INST] [After:O, a:O, successful:O, penetration:O, ,:O, FIN7:B-HackOrg, uses:O, its:O, own:O, backdoors:B-Tool, and:O, the:O, CobaltStrike:B-Tool, framework:I-Tool, or:O, Powershell:B-Tool, Empire:O, components:O, to:O, hop:O, to:O, interesting:O, parts:O, of:O, the:O, network:O, ,:O, where:O, it:O, can:O, monetize:O, its:O, access:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'AveMaria is a new botnet , whose first version we found in September 2018 , right after the arrests of the FIN7 members .' [/INST] [AveMaria:B-HackOrg, is:O, a:O, new:O, botnet:O, ,:O, whose:O, first:O, version:O, we:O, found:O, in:O, September:B-Time, 2018:I-Time, ,:O, right:O, after:O, the:O, arrests:O, of:O, the:O, FIN7:B-HackOrg, members:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This threat actor stole suspected of stealing €13 million from Bank of Valetta , Malta earlier this year .' [/INST] [This:O, threat:B-HackOrg, actor:I-HackOrg, stole:O, suspected:O, of:O, stealing:O, €13:O, million:O, from:O, Bank:B-Org, of:O, Valetta:O, ,:O, Malta:O, earlier:O, this:B-Time, year:I-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In fact , AveMaria is a classic infostealer bot that collects all possible credentials from various types of software: browsers , email clients , messengers , etc , and can act as a keylogger .' [/INST] [In:O, fact:O, ,:O, AveMaria:B-SamFile, is:O, a:O, classic:O, infostealer:O, bot:O, that:O, collects:B-Features, all:O, possible:O, credentials:O, from:O, various:O, types:O, of:O, software::O, browsers:O, ,:O, email:O, clients:O, ,:O, messengers:O, ,:O, etc:O, ,:O, and:O, can:O, act:B-Features, as:O, a:O, keylogger:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'They also use AutoIT droppers , password-protected EXE files and even ISO images .' [/INST] [They:B-HackOrg, also:O, use:O, AutoIT:B-Tool, droppers:I-Tool, ,:O, password-protected:O, EXE:O, files:O, and:O, even:O, ISO:O, images:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'To deliver their malware , the cyber criminals use spearphishing emails with various types of attachments: MS Office documents or spreadsheet files exploiting some known vulnerability like CVE-2017-11882 , or documents with Ole2Link and SCT .' [/INST] [To:O, deliver:O, their:O, malware:O, ,:O, the:O, cyber:B-HackOrg, criminals:I-HackOrg, use:O, spearphishing:B-Tool, emails:I-Tool, with:O, various:O, types:O, of:O, attachments::B-Tool, MS:O, Office:O, documents:B-Tool, or:O, spreadsheet:O, files:O, exploiting:O, some:O, known:O, vulnerability:O, like:O, CVE-2017-11882:B-Exp, ,:O, or:O, documents:B-Tool, with:O, Ole2Link:O, and:O, SCT:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Interestingly , this actor targeted financial entities and companies in one African country , which lead us to think that CopyPaste was associated with cybermercenaries or a training center .' [/INST] [Interestingly:O, ,:O, this:O, actor:B-HackOrg, targeted:O, financial:B-Idus, entities:O, and:O, companies:O, in:O, one:O, African:B-Area, country:O, ,:O, which:O, lead:O, us:O, to:O, think:O, that:O, CopyPaste:O, was:O, associated:O, with:O, cybermercenaries:O, or:O, a:O, training:O, center:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'At the end of 2018 , while searching for new FIN7 campaigns via telemetry , we discovered a set of activity that we temporarily called CopyPaste” from a previously unknown APT .' [/INST] [At:O, the:O, end:B-Time, of:I-Time, 2018:I-Time, ,:O, while:O, searching:O, for:O, new:O, FIN7:B-HackOrg, campaigns:O, via:O, telemetry:O, ,:O, we:O, discovered:O, a:O, set:O, of:O, activity:O, that:O, we:O, temporarily:O, called:O, CopyPaste”:B-OffAct, from:O, a:O, previously:O, unknown:O, APT:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FIN7 and Cobalt used decoy 302 HTTP redirections too , FIN7 on its GRIFFON C2s before January 2018 , and Cobalt , on its staging servers , similar to CopyPaste .' [/INST] [FIN7:B-HackOrg, and:O, Cobalt:B-HackOrg, used:O, decoy:B-Way, 302:O, HTTP:O, redirections:O, too:O, ,:O, FIN7:B-HackOrg, on:O, its:O, GRIFFON:O, C2s:O, before:O, January:B-Time, 2018:I-Time, ,:O, and:O, Cobalt:O, ,:O, on:O, its:O, staging:O, servers:O, ,:O, similar:O, to:O, CopyPaste:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Quite recently , FIN7 threat actors typosquatted the brand Digicert” using the domain name digicert-cdn[.]com , which is used as a command and control server for their GRIFFON implants .' [/INST] [Quite:O, recently:O, ,:O, FIN7:B-HackOrg, threat:O, actors:O, typosquatted:O, the:O, brand:O, Digicert”:B-Org, using:O, the:O, domain:O, name:O, digicert-cdn[.]com:O, ,:O, which:O, is:O, used:O, as:O, a:O, command:B-Tool, and:O, control:B-Tool, server:I-Tool, for:O, their:O, GRIFFON:O, implants:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The first of them is the well-known FIN7 , which specializes in attacking various companies to get access to financial data or PoS infrastructure .' [/INST] [The:O, first:O, of:O, them:O, is:O, the:O, well-known:O, FIN7:B-HackOrg, ,:O, which:O, specializes:O, in:O, attacking:O, various:B-Org, companies:I-Org, to:O, get:B-Purp, access:I-Purp, to:O, financial:B-Idus, data:O, or:O, PoS:O, infrastructure:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The second one is CobaltGoblin Carbanak EmpireMonkey , which uses the same toolkit , techniques and similar infrastructure but targets only financial institutions and associated software/services providers .' [/INST] [The:O, second:O, one:O, is:O, CobaltGoblin:B-HackOrg, Carbanak:B-HackOrg, EmpireMonkey:B-HackOrg, ,:O, which:O, uses:O, the:O, same:O, toolkit:O, ,:O, techniques:O, and:O, similar:O, infrastructure:O, but:O, targets:O, only:O, financial:B-Idus, institutions:O, and:O, associated:O, software/services:O, providers:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We observe , with various level of confidence , that there are several interconnected groups using very similar toolkits and the same infrastructure to conduct their cyberattacks .' [/INST] [We:B-SecTeam, observe:O, ,:O, with:O, various:O, level:O, of:O, confidence:O, ,:O, that:O, there:O, are:O, several:O, interconnected:O, groups:B-HackOrg, using:O, very:O, similar:B-Tool, toolkits:I-Tool, and:O, the:O, same:O, infrastructure:B-Tool, to:O, conduct:O, their:O, cyberattacks:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The last piece is the newly discovered CopyPaste group , who targeted financial entities and companies in one African country , which lead us to think that CopyPaste was associated with cybermercenaries or a training center .' [/INST] [The:O, last:O, piece:O, is:O, the:O, newly:O, discovered:O, CopyPaste:B-HackOrg, group:O, ,:O, who:O, targeted:O, financial:B-Idus, entities:O, and:O, companies:B-Org, in:O, one:O, African:B-Area, country:O, ,:O, which:O, lead:O, us:O, to:O, think:O, that:O, CopyPaste:O, was:O, associated:O, with:O, cybermercenaries:O, or:O, a:O, training:B-Org, center:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'At the end of 2018 , the cluster started to use not only CobaltStrike but also Powershell Empire in order to gain a foothold on the victims’ networks .' [/INST] [At:O, the:O, end:B-Time, of:I-Time, 2018:I-Time, ,:O, the:O, cluster:B-HackOrg, started:O, to:O, use:O, not:O, only:O, CobaltStrike:B-Tool, but:O, also:O, Powershell:B-Tool, Empire:O, in:O, order:O, to:O, gain:O, a:O, foothold:O, on:O, the:O, victims’:O, networks:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FIN7 thus continues to use effective spearphishing campaigns in conjunction with well-known MS Office exploits generated by the framework .' [/INST] [FIN7:B-HackOrg, thus:O, continues:O, to:O, use:O, effective:O, spearphishing:B-Way, campaigns:O, in:O, conjunction:O, with:O, well-known:O, MS:O, Office:O, exploits:O, generated:O, by:O, the:O, framework:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'MuddyWater is widely regarded as a long-lived APT group in the Middle East .' [/INST] [MuddyWater:B-HackOrg, is:O, widely:O, regarded:O, as:O, a:O, long-lived:O, APT:O, group:O, in:O, the:O, Middle:B-Area, East:I-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'From February to April 2019 , MuddyWater launched a series of spear-phishing attacks against governments , educational institutions , financial , telecommunications and defense companies in Turkey , Iran , Afghanistan , Iraq , Tajikistan and Azerbaijan .' [/INST] [From:O, February:O, to:O, April:B-Time, 2019:I-Time, ,:O, MuddyWater:B-HackOrg, launched:O, a:O, series:O, of:O, spear-phishing:B-Way, attacks:O, against:O, governments:B-Org, ,:O, educational:B-Idus, institutions:I-Idus, ,:O, financial:B-Idus, ,:O, telecommunications:B-Idus, and:O, defense:B-Idus, companies:O, in:O, Turkey:B-Area, ,:O, Iran:B-Area, ,:O, Afghanistan:B-Area, ,:O, Iraq:B-Area, ,:O, Tajikistan:B-Area, and:O, Azerbaijan:B-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FIN7 thus continue to use effective spearphishing campaigns in conjunction with well-known MS Office exploits generated by the framework .' [/INST] [FIN7:B-HackOrg, thus:O, continue:O, to:O, use:O, effective:O, spearphishing:B-Way, campaigns:O, in:O, conjunction:O, with:O, well-known:O, MS:O, Office:O, exploits:O, generated:O, by:O, the:O, framework:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We also unearthed and detailed our other findings on MuddyWater , such as its connection to four Android malware variants and its use of false flag techniques , among others , in our report New MuddyWater Activities Uncovered: Threat Actors Used Multi-Stage Backdoors , False Flags , Android Malware , and More .' [/INST] [We:O, also:O, unearthed:O, and:O, detailed:O, our:O, other:O, findings:O, on:O, MuddyWater:B-HackOrg, ,:O, such:O, as:O, its:O, connection:O, to:O, four:O, Android:B-Tool, malware:I-Tool, variants:O, and:O, its:O, use:O, of:O, false:O, flag:O, techniques:O, ,:O, among:O, others:O, ,:O, in:O, our:O, report:O, New:O, MuddyWater:O, Activities:O, Uncovered::O, Threat:O, Actors:O, Used:O, Multi-Stage:B-Tool, Backdoors:I-Tool, ,:O, False:B-Tool, Flags:I-Tool, ,:O, Android:B-Tool, Malware:I-Tool, ,:O, and:O, More:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Instead , the campaign used compromised legitimate accounts to trick victims into installing malware .' [/INST] [Instead:O, ,:O, the:O, campaign:B-OffAct, used:O, compromised:B-Tool, legitimate:I-Tool, accounts:I-Tool, to:O, trick:O, victims:O, into:O, installing:B-Way, malware:I-Way, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Notably , the group’s use of email as infection vector seems to yield success for their campaigns .' [/INST] [Notably:O, ,:O, the:O, group’s:B-HackOrg, use:O, of:O, email:B-Tool, as:O, infection:O, vector:O, seems:O, to:O, yield:O, success:O, for:O, their:O, campaigns:B-OffAct, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We also observed MuddyWater’s use of multiple open source post-exploitation tools , which they deployed after successfully compromising a target .' [/INST] [We:O, also:O, observed:O, MuddyWater’s:B-HackOrg, use:O, of:O, multiple:O, open:O, source:O, post-exploitation:B-Tool, tools:I-Tool, ,:O, which:O, they:O, deployed:O, after:O, successfully:O, compromising:O, a:O, target:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The attacker also connected to the compromised servers from IP addresses that were linked to dynamic domain names used as C&Cs by the delivered payloads .' [/INST] [The:O, attacker:B-HackOrg, also:O, connected:B-Way, to:O, the:O, compromised:O, servers:O, from:O, IP:O, addresses:O, that:O, were:O, linked:O, to:O, dynamic:O, domain:O, names:O, used:O, as:O, C&Cs:O, by:O, the:O, delivered:B-Tool, payloads:I-Tool, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The main payload is usually Imminent Monitor RAT; however , at the beginning of 2018 , we also observed the use of LuminosityLink RAT , NetWire RAT , and NjRAT .' [/INST] [The:O, main:O, payload:O, is:O, usually:O, Imminent:O, Monitor:B-SamFile, RAT;:I-SamFile, however:O, ,:O, at:O, the:O, beginning:B-Time, of:I-Time, 2018:I-Time, ,:O, we:O, also:O, observed:O, the:O, use:O, of:O, LuminosityLink:B-SamFile, RAT:I-SamFile, ,:O, NetWire:B-SamFile, RAT:I-SamFile, ,:O, and:O, NjRAT:B-SamFile, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In a case in June 2019 , we also noticed Warzone RAT being used .' [/INST] [In:O, a:O, case:O, in:O, June:B-Time, 2019:I-Time, ,:O, we:O, also:O, noticed:O, Warzone:B-SamFile, RAT:I-SamFile, being:O, used:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Xpert RAT reportedly first appeared in 2011 .' [/INST] [Xpert:B-SamFile, RAT:I-SamFile, reportedly:O, first:O, appeared:O, in:O, 2011:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The first version of Proyecto RAT” was published at the end of 2010 .' [/INST] [The:O, first:O, version:O, of:O, Proyecto:B-SamFile, RAT”:I-SamFile, was:O, published:O, at:O, the:O, end:O, of:O, 2010:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'But with the West African gang we’ve named Scattered Canary , we have a deeper look at how business email compromise is connected to the rest of the cybercrime .' [/INST] [But:O, with:O, the:O, West:O, African:B-Area, gang:O, we’ve:O, named:O, Scattered:B-HackOrg, Canary:I-HackOrg, ,:O, we:O, have:O, a:O, deeper:O, look:O, at:O, how:O, business:O, email:O, compromise:O, is:O, connected:B-Way, to:O, the:O, rest:O, of:O, the:O, cybercrime:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In a recent report , the FBI’s Internet Crime Complaint Center (IC3) reported that more than 20 , 000 businesses lost nearly $1.3 billion to BEC attacks in 2018 .' [/INST] [In:O, a:O, recent:O, report:O, ,:O, the:O, FBI’s:B-SecTeam, Internet:O, Crime:O, Complaint:O, Center:O, (IC3):O, reported:O, that:O, more:O, than:O, 20:O, ,:O, 000:O, businesses:B-Idus, lost:O, nearly:O, $1.3:O, billion:O, to:O, BEC:B-OffAct, attacks:I-OffAct, in:O, 2018:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This investigation by the Agari Cyber Intelligence Division into the cybercriminal group we’ve named Scattered Canary offers unprecedented visibility into eleven years of fraud and criminal activities , and the growth of a 419 startup into a fully operational BEC business .' [/INST] [This:O, investigation:O, by:O, the:O, Agari:B-SecTeam, Cyber:I-SecTeam, Intelligence:I-SecTeam, Division:O, into:O, the:O, cybercriminal:O, group:B-HackOrg, we’ve:O, named:O, Scattered:B-HackOrg, Canary:I-HackOrg, offers:O, unprecedented:O, visibility:O, into:O, eleven:O, years:O, of:O, fraud:O, and:O, criminal:O, activities:O, ,:O, and:O, the:O, growth:O, of:O, a:O, 419:O, startup:O, into:O, a:O, fully:O, operational:O, BEC:O, business:B-Idus, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While this criminal organization’s activities now center around BEC , and extend to romance scams , credit card fraud , check fraud , fake job listings , credential harvesting , tax schemes , and more , these actors came from much humbler beginnings , starting with basic Craigslist scams in 2008 .' [/INST] [While:O, this:O, criminal:O, organization’s:B-SecTeam, activities:O, now:O, center:O, around:O, BEC:O, ,:O, and:O, extend:O, to:O, romance:B-OffAct, scams:I-OffAct, ,:O, credit:B-OffAct, card:I-OffAct, fraud:I-OffAct, ,:O, check:B-OffAct, fraud:I-OffAct, ,:O, fake:B-OffAct, job:I-OffAct, listings:O, ,:O, credential:B-OffAct, harvesting:I-OffAct, ,:O, tax:B-OffAct, schemes:I-OffAct, ,:O, and:O, more:O, ,:O, these:O, actors:O, came:O, from:O, much:O, humbler:O, beginnings:O, ,:O, starting:O, with:O, basic:O, Craigslist:O, scams:O, in:O, 2008:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'On November 29 , 2018 , Scattered Canary sent an attack email to Agari CFO Raymond Lim , enquiring as to his availability to send out a domestic wire transfer .' [/INST] [On:O, November:B-Time, 29:I-Time, ,:O, 2018:B-Time, ,:O, Scattered:B-HackOrg, Canary:I-HackOrg, sent:O, an:O, attack:O, email:O, to:O, Agari:O, CFO:O, Raymond:O, Lim:O, ,:O, enquiring:O, as:O, to:O, his:O, availability:O, to:O, send:O, out:O, a:O, domestic:O, wire:O, transfer:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Many feel that they have a home team advantage living in Nigeria , where they are free to pay off law enforcement to look the other way .' [/INST] [Many:O, feel:O, that:O, they:O, have:O, a:O, home:O, team:O, advantage:O, living:O, in:O, Nigeria:B-Area, ,:O, where:O, they:B-HackOrg, are:O, free:O, to:O, pay:O, off:O, law:O, enforcement:O, to:O, look:O, the:O, other:O, way:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Scattered Canary’s fraudulent history can be traced as far back as October 2008 , when the group first arrived on the cybercriminal circuit .' [/INST] [Scattered:B-HackOrg, Canary’s:I-HackOrg, fraudulent:O, history:O, can:O, be:O, traced:O, as:O, far:O, back:O, as:O, October:B-Time, 2008:I-Time, ,:O, when:O, the:O, group:B-HackOrg, first:O, arrived:O, on:O, the:O, cybercriminal:O, circuit:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'By March 2016 , one of Scattered Canary’s members had built enough trust with a romance victim—who we’ll call Jane—that she became a frequent source of new mule accounts for the group .' [/INST] [By:O, March:B-Time, 2016:I-Time, ,:O, one:O, of:O, Scattered:B-HackOrg, Canary’s:I-HackOrg, members:O, had:O, built:O, enough:O, trust:O, with:O, a:O, romance:O, victim—who:O, we’ll:O, call:O, Jane—that:O, she:O, became:O, a:O, frequent:O, source:O, of:O, new:O, mule:O, accounts:O, for:O, the:O, group:B-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Alpha’s early role was fairly simple: engage with individuals , who he chose based on the goods they were selling , and then provide personal shipping addresses back to Omega .' [/INST] [Alpha’s:B-HackOrg, early:O, role:O, was:O, fairly:O, simple::O, engage:O, with:O, individuals:O, ,:O, who:O, he:O, chose:O, based:O, on:O, the:O, goods:O, they:O, were:O, selling:O, ,:O, and:O, then:O, provide:B-Purp, personal:I-Purp, shipping:I-Purp, addresses:O, back:O, to:O, Omega:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'By all accounts , late 2015 was the beginning of BEC for Scattered Canary .' [/INST] [By:O, all:O, accounts:O, ,:O, late:B-Time, 2015:I-Time, was:O, the:O, beginning:O, of:O, BEC:O, for:O, Scattered:B-HackOrg, Canary:I-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The first type of attack Scattered Canary pivoted to was credential phishing .' [/INST] [The:O, first:O, type:O, of:O, attack:O, Scattered:B-HackOrg, Canary:I-HackOrg, pivoted:O, to:O, was:O, credential:B-Way, phishing:I-Way, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Between July 2015 and February 2016 , Scattered Canary’s primary focus seemed to be mass harvesting general credentials using a Google Docs phishing page .' [/INST] [Between:O, July:B-Time, 2015:I-Time, and:O, February:B-Time, 2016:I-Time, ,:O, Scattered:B-HackOrg, Canary’s:I-HackOrg, primary:O, focus:O, seemed:O, to:O, be:O, mass:O, harvesting:O, general:O, credentials:O, using:O, a:O, Google:O, Docs:O, phishing:B-Way, page:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In the first few months of their credential phishing ventures , Scattered Canary’s sights were mostly set on Asian targets—Malaysia and Japan , in particular .' [/INST] [In:O, the:O, first:O, few:O, months:O, of:O, their:O, credential:O, phishing:B-Way, ventures:O, ,:O, Scattered:B-HackOrg, Canary’s:I-HackOrg, sights:O, were:O, mostly:O, set:O, on:O, Asian:B-Area, targets—Malaysia:B-Area, and:O, Japan:B-Area, ,:O, in:O, particular:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In November 2015 , the group started to focus on North American users , mostly in the United States .' [/INST] [In:O, November:B-Time, 2015:I-Time, ,:O, the:O, group:B-HackOrg, started:O, to:O, focus:O, on:O, North:B-Area, American:I-Area, users:O, ,:O, mostly:O, in:O, the:O, United:B-Area, States:I-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This activity ceased in February 2016 , likely because the men who made up Scattered Canary began to focus on honing their BEC total , Scattered Canary received more than 3 , 000 account credentials as a result of their phishing attacks .' [/INST] [This:O, activity:O, ceased:O, in:O, February:B-Time, 2016:I-Time, ,:O, likely:O, because:O, the:O, men:O, who:O, made:O, up:O, Scattered:B-HackOrg, Canary:I-HackOrg, began:O, to:O, focus:O, on:O, honing:O, their:O, BEC:O, total:O, ,:O, Scattered:B-HackOrg, Canary:I-HackOrg, received:O, more:O, than:O, 3:O, ,:O, 000:O, account:O, credentials:O, as:O, a:O, result:O, of:O, their:O, phishing:B-Exp, attacks:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'For over eighteen months from March 2017 until November 2018 , Scattered Canary’s frequent enterprise-focused credential phishing campaigns almost exclusively targeted businesses in the United States and Canada .' [/INST] [For:O, over:O, eighteen:O, months:O, from:O, March:B-Time, 2017:I-Time, until:O, November:B-Time, 2018:I-Time, ,:O, Scattered:B-HackOrg, Canary’s:I-HackOrg, frequent:O, enterprise-focused:O, credential:O, phishing:B-OffAct, campaigns:I-OffAct, almost:O, exclusively:O, targeted:O, businesses:O, in:O, the:B-Area, United:I-Area, States:I-Area, and:O, Canada:B-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In July 2018 , following a trend we have observed across the entire BEC threat landscape , Scattered Canary changed their preferred cash out mechanism from wire transfers to gift cards .' [/INST] [In:O, July:B-Time, 2018:I-Time, ,:O, following:O, a:O, trend:O, we:O, have:O, observed:O, across:O, the:O, entire:O, BEC:O, threat:O, landscape:O, ,:O, Scattered:B-HackOrg, Canary:I-HackOrg, changed:O, their:O, preferred:O, cash:O, out:O, mechanism:O, from:O, wire:O, transfers:O, to:O, gift:O, cards:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Instead of using fake Google Docs phishing pages to collect personal email login credentials , Scattered Canary began using phishing pages of commonly used business applications to compromise enterprise credentials .' [/INST] [Instead:O, of:O, using:O, fake:O, Google:O, Docs:O, phishing:B-Way, pages:O, to:O, collect:B-Purp, personal:O, email:O, login:O, credentials:O, ,:O, Scattered:B-HackOrg, Canary:I-HackOrg, began:O, using:O, phishing:B-Way, pages:O, of:O, commonly:O, used:O, business:O, applications:O, to:O, compromise:B-Purp, enterprise:I-Purp, credentials:I-Purp, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Using personal information obtained from various sources , Scattered Canary started perpetrating fraud against US federal and state government agencies .' [/INST] [Using:O, personal:O, information:O, obtained:O, from:O, various:O, sources:O, ,:O, Scattered:B-HackOrg, Canary:I-HackOrg, started:O, perpetrating:O, fraud:O, against:O, US:B-Area, federal:O, and:O, state:B-Org, government:I-Org, agencies:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In total , 35 actors have been tied to Scattered Canary’s operations since the group emerged in 2008 .' [/INST] [In:O, total:O, ,:O, 35:O, actors:O, have:O, been:O, tied:O, to:O, Scattered:B-HackOrg, Canary’s:I-HackOrg, operations:O, since:O, the:O, group:O, emerged:O, in:O, 2008:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Just as with romance scams , actors make use of scripts and templates they can copy-and-paste without having to create something on their own .' [/INST] [Just:O, as:O, with:O, romance:O, scams:O, ,:O, actors:B-HackOrg, make:O, use:O, of:O, scripts:B-Tool, and:O, templates:B-Tool, they:O, can:O, copy-and-paste:O, without:O, having:O, to:O, create:O, something:O, on:O, their:O, own:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'When it comes to engaging targets , Scattered Canary frequently maximized efficiencies through the use of scripts , or as some members of the group call them , formats.” These formats are templated text documents that can contain several layers of phishing messages to send to potential victims .' [/INST] [When:O, it:O, comes:O, to:O, engaging:O, targets:O, ,:O, Scattered:B-HackOrg, Canary:I-HackOrg, frequently:O, maximized:O, efficiencies:O, through:O, the:O, use:O, of:O, scripts:O, ,:O, or:O, as:O, some:O, members:O, of:O, the:O, group:O, call:O, them:O, ,:O, formats.”:O, These:O, formats:O, are:O, templated:O, text:O, documents:O, that:O, can:O, contain:O, several:O, layers:O, of:O, phishing:B-Way, messages:O, to:O, send:O, to:O, potential:O, victims:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Recently , we unveiled the existence of a UEFI rootkit , called LoJax , which we attribute to the Sednit group .' [/INST] [Recently:O, ,:O, we:O, unveiled:O, the:O, existence:O, of:O, a:O, UEFI:O, rootkit:O, ,:O, called:O, LoJax:B-Tool, ,:O, which:O, we:O, attribute:O, to:O, the:O, Sednit:B-HackOrg, group:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'If Scattered Canary can be seen as a microcosm for the rapidly evolving organizations behind today’s most pernicious email scams , this report demonstrates that a much more holistic approach—one based on threat actor identity rather than type of fraudulent activity—is required to detect email fraud and protect organizations .' [/INST] [If:O, Scattered:B-HackOrg, Canary:I-HackOrg, can:O, be:O, seen:O, as:O, a:O, microcosm:O, for:O, the:O, rapidly:O, evolving:O, organizations:O, behind:O, today’s:O, most:O, pernicious:O, email:B-Way, scams:I-Way, ,:O, this:O, report:O, demonstrates:O, that:O, a:O, much:O, more:O, holistic:O, approach—one:O, based:O, on:O, threat:O, actor:O, identity:O, rather:O, than:O, type:O, of:O, fraudulent:O, activity—is:O, required:O, to:O, detect:O, email:O, fraud:O, and:O, protect:O, organizations:O, .:O] </s> |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.