Datasets:
pritamdeka
/
dataset_dnrti_train

Dataset card Data Studio Files
xet
Community
1
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306
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<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: 'Leafminer also utilized Process Doppelganging , a detection evasion technique first discussed at the Black Hat EU conference last year .' [/INST] [Leafminer:B-HackOrg, also:O, utilized:O, Process:B-Way, Doppelganging:I-Way, ,:O, a:O, detection:O, evasion:O, technique:O, first:O, discussed:O, at:O, the:O, Black:O, Hat:O, EU:O, conference:O, last:O, year: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: 'Dragos has identified Leafminer group targeting access operations in the electric utility sector .' [/INST] [Dragos:B-SecTeam, has:O, identified:O, Leafminer:B-HackOrg, group:I-HackOrg, targeting:O, access:O, operations:O, in:O, the:O, electric:B-Org, utility:I-Org, sector: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: 'Analysis of RASPITE tactics , techniques , and procedures ( TTPs ) indicate the group has been active in some form since early - to mid-2017 .' [/INST] [Analysis:O, of:O, RASPITE:B-HackOrg, tactics:O, ,:O, techniques:O, ,:O, and:O, procedures:O, (:O, TTPs:O, ):O, indicate:O, the:O, group:B-HackOrg, has:O, been:O, active:O, in:O, some:O, form:O, since:O, early:O, -:O, to:O, mid-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: 'RASPITE targeting includes entities in the US , Middle East , Europe , and East Asia .' [/INST] [RASPITE:B-HackOrg, targeting:O, includes:O, entities:O, in:O, the:O, US:B-Area, ,:O, Middle:B-Area, East:I-Area, ,:O, Europe:B-Area, ,:O, and:O, East:B-Area, Asia: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: 'RASPITE overlaps significantly with Symantec 's Leafminer , which recently released a report on the group 's activity in the Middle East .' [/INST] [RASPITE:B-HackOrg, overlaps:O, significantly:O, with:O, Symantec:B-SecTeam, 's:O, Leafminer:B-HackOrg, ,:O, which:O, recently:O, released:O, a:O, report:O, on:O, the:O, group:B-HackOrg, 's:O, activity:B-OffAct, 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: 'RASPITE 's activity to date currently focuses on initial access operations within the electric utility sector .' [/INST] [RASPITE:B-HackOrg, 's:O, activity:B-OffAct, to:O, date:O, currently:O, focuses:O, on:O, initial:O, access:O, operations:O, within:O, the:O, electric:B-Org, utility:I-Org, sector: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: 'This means that the Leafminer group is targeting electric utilities .' [/INST] [This:O, means:O, that:O, the:O, Leafminer:B-HackOrg, group:I-HackOrg, is:O, targeting:O, electric:B-Org, utilities: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: 'While the group has not yet demonstrated an ICS capability , RASPITE 's recent targeting focus and methodology are clear indicators of necessary activity for initial intrusion operations into an IT network to prepare the way for later potential ICS events .' [/INST] [While:O, the:O, group:B-HackOrg, has:O, not:O, yet:O, demonstrated:O, an:O, ICS:B-Tool, capability:O, ,:O, RASPITE:B-HackOrg, 's:O, recent:O, targeting:O, focus:O, and:O, methodology:O, are:O, clear:B-Purp, indicators:I-Purp, of:I-Purp, necessary:I-Purp, activity:I-Purp, for:O, initial:O, intrusion:O, operations:O, into:O, an:O, IT:B-Idus, network:O, to:O, prepare:O, the:O, way:O, for:O, later:O, potential:O, ICS:B-Tool, events: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: 'Active since at least 2014 , this actor has long-standing interest in maritime industries , naval defense contractors , and associated research institutions in the United States and Western Europe .' [/INST] [Active:O, since:O, at:O, least:O, 2014:B-Time, ,:O, this:O, actor:B-HackOrg, has:O, long-standing:O, interest:O, in:O, maritime:B-Idus, industries:I-Idus, ,:O, naval:B-Org, defense:I-Org, contractors:I-Org, ,:O, and:O, associated:O, research:B-Org, institutions:I-Org, in:O, the:O, United:B-Area, States:I-Area, and:O, Western:B-Area, Europe: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: 'Active since at least 2014 , the Leviathan has long-standing interest in maritime industries , naval defense contractors , and associated research institutions in the United States and Western Europe .' [/INST] [Active:O, since:O, at:O, least:O, 2014:B-Time, ,:O, the:O, Leviathan:B-HackOrg, has:O, long-standing:O, interest:O, in:O, maritime:B-Idus, industries:I-Idus, ,:O, naval:B-Org, defense:I-Org, contractors:I-Org, ,:O, and:O, associated:O, research:B-Org, institutions:I-Org, in:O, the:O, United:B-Area, States:I-Area, and:O, Western:B-Area, Europe: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: 'On September 15 and 19 , 2017 , Proofpoint detected and blocked spearphishing emails from this group targeting a US shipbuilding company and a US university research center with military ties .' [/INST] [On:O, September:B-Time, 15:I-Time, and:O, 19:B-Time, ,:I-Time, 2017:I-Time, ,:O, Proofpoint:B-SecTeam, detected:O, and:O, blocked:O, spearphishing:B-Way, emails:I-Way, from:O, this:O, group:B-HackOrg, targeting:O, a:O, US:B-Area, shipbuilding:B-Org, company:I-Org, and:O, a:O, US:B-Area, university:O, research:O, center:O, with:O, military:B-Idus, ties: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 attachments exploited CVE-2017-8759 which was discovered and documented only five days prior to the campaign .' [/INST] [The:O, attachments:O, exploited:O, CVE-2017-8759:B-Exp, which:O, was:O, discovered:O, and:O, documented:O, only:O, five:O, days:O, prior:O, to:O, the:O, campaign: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: 'Some of the documents exploited CVE-2017-0199 to deliver the payload .' [/INST] [Some:O, of:O, the:O, documents:B-SamFile, exploited:O, CVE-2017-0199:B-Exp, to:O, deliver:B-Features, the:I-Features, payload:I-Features, .: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 August 2 and 4 , the actor sent targeted spearphishing emails containing malicious URLs linking to documents to multiple defense contractors .' [/INST] [Between:O, August:B-Time, 2:I-Time, and:O, 4:I-Time, ,:O, the:O, actor:B-HackOrg, sent:O, targeted:O, spearphishing:B-Way, emails:I-Way, containing:O, malicious:O, URLs:O, linking:O, to:O, documents:O, to:O, multiple:O, defense:B-Org, contractors: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: 'Between August 2 and 4 , the Leviathan sent targeted spearphishing emails containing malicious URLs linking to documents to multiple defense contractors .' [/INST] [Between:O, August:B-Time, 2:I-Time, and:O, 4:I-Time, ,:O, the:O, Leviathan:B-HackOrg, sent:O, targeted:O, spearphishing:B-Way, emails:I-Way, containing:O, malicious:O, URLs:O, linking:O, to:O, documents:O, to:O, multiple:O, defense:B-Org, contractors: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: 'The Leviathan also occasionally used macro-laden Microsoft Word documents to target other US research and development organizations during this period .' [/INST] [The:O, Leviathan:B-HackOrg, also:O, occasionally:O, used:O, macro-laden:B-SamFile, Microsoft:I-SamFile, Word:I-SamFile, documents:I-SamFile, to:O, target:O, other:O, US:B-Area, research:O, and:O, development:B-Org, organizations:I-Org, during:O, this:O, period: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 period between November 2014 and January 2015 marked one of the earlier instances in which Proofpoint observed persistent exploitation attempts by this actor .' [/INST] [The:O, period:O, between:O, November:B-Time, 2014:I-Time, and:O, January:B-Time, 2015:I-Time, marked:O, one:O, of:O, the:O, earlier:O, instances:O, in:O, which:O, Proofpoint:B-SecTeam, observed:O, persistent:O, exploitation:O, attempts:O, by:O, this:O, actor: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: 'The Leviathan , whose espionage activities primarily focus on targets in the US and Western Europe with military ties , has been active since at least 2014 .' [/INST] [The:O, Leviathan:B-HackOrg, ,:O, whose:O, espionage:B-OffAct, activities:I-OffAct, primarily:O, focus:O, on:O, targets:O, in:O, the:O, US:B-Area, and:O, Western:B-Area, Europe:I-Area, with:O, military:B-Idus, ties:O, ,:O, has:O, been:O, active:O, since:O, at:O, least:O, 2014: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 actor , whose espionage activities primarily focus on targets in the US and Western Europe with military ties , has been active since at least 2014 .' [/INST] [This:O, actor:B-HackOrg, ,:O, whose:O, espionage:B-OffAct, activities:I-OffAct, primarily:O, focus:O, on:O, targets:O, in:O, the:O, US:B-Area, and:O, Western:B-Area, Europe:I-Area, with:O, military:B-Idus, ties:O, ,:O, has:O, been:O, active:O, since:O, at:O, least:O, 2014: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 campaign is linked to a group of suspected Chinese cyber espionage actors we have tracked since 2013 , dubbed TEMP.Periscope .' [/INST] [The:O, campaign:B-OffAct, is:O, linked:O, to:O, a:O, group:B-HackOrg, of:O, suspected:O, Chinese:B-Area, cyber:B-HackOrg, espionage:I-HackOrg, actors:I-HackOrg, we:O, have:O, tracked:O, since:O, 2013:B-Time, ,:O, dubbed:O, TEMP.Periscope: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: 'The Leviathan generally emailed Microsoft Excel documents with malicious macros to US universities with military interests , most frequently related to the Navy .' [/INST] [The:O, Leviathan:B-HackOrg, generally:O, emailed:O, Microsoft:B-Way, Excel:I-Way, documents:I-Way, with:O, malicious:O, macros:O, to:O, US:B-Area, universities:B-Idus, with:O, military:B-Idus, interests:O, ,:O, most:O, frequently:O, related:O, to:O, the:O, Navy:B-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: 'The current campaign is a sharp escalation of detected activity since summer 2017 .' [/INST] [The:O, current:O, campaign:B-OffAct, is:O, a:O, sharp:O, escalation:O, of:O, detected:O, activity:B-OffAct, since:O, summer:B-Time, 2017: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: 'Since early 2018 , FireEye ( including our FireEye as a Service ( FaaS ) , Mandiant Consulting , and iSIGHT Intelligence teams ) has been tracking an ongoing wave of intrusions targeting engineering and maritime entities , especially those connected to South China Sea issues .' [/INST] [Since:O, early:B-Time, 2018:I-Time, ,:O, FireEye:B-SecTeam, (:O, including:O, our:O, FireEye:B-SecTeam, as:O, a:O, Service:O, (:O, FaaS:O, ):O, ,:O, Mandiant:B-SecTeam, Consulting:I-SecTeam, ,:O, and:O, iSIGHT:B-SecTeam, Intelligence:I-SecTeam, teams:O, ):O, has:O, been:O, tracking:O, an:O, ongoing:O, wave:O, of:O, intrusions:O, targeting:O, engineering:B-Idus, and:O, maritime:B-Org, entities:I-Org, ,:O, especially:O, those:O, connected:O, to:O, South:B-Area, China:I-Area, Sea:I-Area, issues: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: 'Known targets of the Leviathan have been involved in the maritime industry , and research institutes , academic organizations , and private firms in the United States .' [/INST] [Known:O, targets:O, of:O, the:O, Leviathan:B-HackOrg, have:O, been:O, involved:O, in:O, the:O, maritime:B-Idus, industry:I-Idus, ,:O, and:O, research:B-Org, institutes:I-Org, ,:O, academic:B-Org, organizations:I-Org, ,:O, and:O, private:B-Org, firms:I-Org, 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: 'Active since at least 2013 , TEMP.Periscope has primarily focused on maritime-related targets across multiple verticals , including engineering firms , shipping and transportation , manufacturing , defense , government offices , and research universities .' [/INST] [Active:O, since:O, at:O, least:O, 2013:B-Time, ,:O, TEMP.Periscope:B-HackOrg, has:O, primarily:O, focused:O, on:O, maritime-related:O, targets:O, across:O, multiple:O, verticals:O, ,:O, including:O, engineering:B-Org, firms:I-Org, ,:O, shipping:B-Idus, and:O, transportation:B-Idus, ,:O, manufacturing:B-Idus, ,:O, defense:B-Idus, ,:O, government:B-Org, offices:I-Org, ,:O, and:O, research:B-Idus, universities: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: 'TEMP.Periscope overlaps in targeting , as well as tactics , techniques , and procedures ( TTPs ) , with TEMP.Jumper , a group that also overlaps significantly with public reporting on NanHaiShu .' [/INST] [TEMP.Periscope:B-HackOrg, overlaps:O, in:O, targeting:O, ,:O, as:O, well:O, as:O, tactics:O, ,:O, techniques:O, ,:O, and:O, procedures:O, (:O, TTPs:O, ):O, ,:O, with:O, TEMP.Jumper:B-HackOrg, ,:O, a:O, group:B-HackOrg, that:O, also:O, overlaps:O, significantly:O, with:O, public:O, reporting:O, on:O, NanHaiShu:B-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 actor has conducted operations since at least 2013 in support of China 's naval modernization effort .' [/INST] [The:O, actor:B-HackOrg, has:O, conducted:O, operations:O, since:O, at:O, least:O, 2013:B-Time, in:O, support:O, of:O, China:B-Area, 's:O, naval:O, modernization:O, effort: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: 'FireEye is highlighting a cyber espionage operation targeting crucial technologies and traditional intelligence targets from a China-nexus state sponsored actor we call APT40 .' [/INST] [FireEye:B-SecTeam, is:O, highlighting:O, a:O, cyber:O, espionage:O, operation:O, targeting:O, crucial:B-Purp, technologies:I-Purp, and:O, traditional:B-Purp, intelligence:I-Purp, targets:O, from:O, a:O, China-nexus:B-Area, state:O, sponsored:O, actor:B-HackOrg, we:O, call:O, APT40: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: 'The Leviathan group has specifically targeted engineering , transportation , and the defense industry , especially where these sectors overlap with maritime technologies .' [/INST] [The:O, Leviathan:B-HackOrg, group:I-HackOrg, has:O, specifically:O, targeted:O, engineering:B-Idus, ,:O, transportation:B-Idus, ,:O, and:O, the:O, defense:B-Idus, industry:I-Idus, ,:O, especially:O, where:O, these:O, sectors:O, overlap:O, with:O, maritime:O, technologies: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 APT40 's emphasis on maritime issues and naval technology ultimately support China 's ambition to establish a blue-water navy .' [/INST] [We:O, believe:O, APT40:B-HackOrg, 's:O, emphasis:O, on:O, maritime:O, issues:O, and:O, naval:B-Idus, technology:I-Idus, ultimately:O, support:O, China:B-Area, 's:O, ambition:O, to:O, establish:O, a:O, blue-water:O, navy: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: 'Within a year APT40 was observed masquerading as a UUV manufacturer , and targeting universities engaged in naval research .' [/INST] [Within:O, a:O, year:O, APT40:B-HackOrg, was:O, observed:O, masquerading:O, as:O, a:O, UUV:O, manufacturer:O, ,:O, and:O, targeting:O, universities:B-Idus, engaged:O, in:O, naval:O, research: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: 'APT40 engages in broader regional targeting against traditional intelligence targets , especially organizations with operations in Southeast Asia .' [/INST] [APT40:B-HackOrg, engages:O, in:O, broader:O, regional:O, targeting:O, against:O, traditional:B-Purp, intelligence:I-Purp, targets:I-Purp, ,:O, especially:O, organizations:O, with:O, operations:O, in:O, Southeast:B-Area, Asia: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: 'We assess with moderate confidence that APT40 is a state-sponsored Chinese cyber espionage operation .' [/INST] [We:O, assess:O, with:O, moderate:O, confidence:O, that:O, APT40:B-HackOrg, is:O, a:O, state-sponsored:O, Chinese:B-Area, cyber:O, espionage:O, operation: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 actor 's targeting is consistent with Chinese state interests and there are multiple technical artifacts indicating the actor is based in China .' [/INST] [The:O, actor:B-HackOrg, 's:O, targeting:O, is:O, consistent:O, with:O, Chinese:B-Area, state:O, interests:O, and:O, there:O, are:O, multiple:O, technical:O, artifacts:O, indicating:O, the:O, actor:B-HackOrg, is:O, based:O, in:O, China: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: 'Analysis of the operational times of the group 's activities indicates that it is probably centered around China Standard Time ( UTC +8 ) .' [/INST] [Analysis:O, of:O, the:O, operational:O, times:O, of:O, the:O, group:B-HackOrg, 's:O, activities:B-OffAct, indicates:O, that:O, it:O, is:O, probably:O, centered:O, around:O, China:B-Area, Standard:O, Time:O, (:O, UTC:O, +8:O, ):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: 'APT40 relies heavily on web shells for an initial foothold into an organization .' [/INST] [APT40:B-HackOrg, relies:O, heavily:O, on:O, web:B-Tool, shells:I-Tool, for:O, an:O, initial:O, foothold:O, into:O, an:O, organization: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: 'APT40 has been observed leveraging a variety of techniques for initial compromise , including web server exploitation , phishing campaigns delivering publicly available and custom backdoors , and strategic web compromises .' [/INST] [APT40:B-HackOrg, has:O, been:O, observed:O, leveraging:O, a:O, variety:O, of:O, techniques:O, for:O, initial:O, compromise:O, ,:O, including:O, web:O, server:O, exploitation:O, ,:O, phishing:B-OffAct, campaigns:I-OffAct, delivering:O, publicly:O, available:O, and:O, custom:O, backdoors:O, ,:O, and:O, strategic:B-Way, web:I-Way, compromises: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: 'Depending on placement , a web shell can provide continued access to victims ' environments , re-infect victim systems , and facilitate lateral movement .' [/INST] [Depending:O, on:O, placement:O, ,:O, a:O, web:O, shell:O, can:O, provide:B-Features, continued:I-Features, access:I-Features, to:O, victims:O, ':O, environments:O, ,:O, re-infect:B-Features, victim:I-Features, systems:I-Features, ,:O, and:O, facilitate:B-Features, lateral:I-Features, movement:I-Features, .: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 capabilities are more than the much discussed CVE-2012-0158 exploits over the past few years .' [/INST] [The:O, group:B-HackOrg, 's:O, capabilities:O, are:O, more:O, than:O, the:O, much:O, discussed:O, CVE-2012-0158:B-Exp, exploits:O, over:O, the:O, past:O, few:O, years: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: 'A paper released today by our colleagues at Palo Alto Networks presented a portion of data on this crew under the label , the Lotus Blossom Operation , , likely named for the debug string present in much of the , Elise , codebase since at least 2012 : , d:\lstudio\projects\lotus\… , .' [/INST] [A:O, paper:O, released:O, today:O, by:O, our:O, colleagues:O, at:O, Palo:B-SecTeam, Alto:I-SecTeam, Networks:I-SecTeam, presented:O, a:O, portion:O, of:O, data:O, on:O, this:O, crew:O, under:O, the:O, label:O, ,:O, the:O, Lotus:B-OffAct, Blossom:I-OffAct, Operation:I-OffAct, ,:O, ,:O, likely:O, named:O, for:O, the:O, debug:O, string:O, present:O, in:O, much:O, of:O, the:O, ,:O, Elise:B-Tool, ,:O, codebase:O, since:O, at:O, least:O, 2012:B-Time, ::O, ,:O, d:\lstudio\projects\lotus\…:O, ,: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 Spring Dragon group is known to have employed spearphish exploits , strategic web compromises , and watering holes attack .' [/INST] [Instead:O, ,:O, the:O, Spring:B-HackOrg, Dragon:I-HackOrg, group:I-HackOrg, is:O, known:O, to:O, have:O, employed:O, spearphish:B-Exp, exploits:I-Exp, ,:O, strategic:B-Way, web:I-Way, compromises:I-Way, ,:O, and:O, watering:B-OffAct, holes:I-OffAct, attack:I-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: 'The group 's spearphish toolset includes PDF exploits , Adobe Flash Player exploits , and the common CVE-2012-0158 Word exploits including those generated from the infamous , Tran Duy Linh , kit .' [/INST] [The:O, group:B-HackOrg, 's:O, spearphish:O, toolset:O, includes:O, PDF:B-Exp, exploits:I-Exp, ,:O, Adobe:B-Exp, Flash:I-Exp, Player:I-Exp, exploits:I-Exp, ,:O, and:O, the:O, common:O, CVE-2012-0158:B-Exp, Word:B-Exp, exploits:I-Exp, including:O, those:O, generated:O, from:O, the:O, infamous:O, ,:O, Tran:B-Tool, Duy:I-Tool, Linh:I-Tool, ,:O, kit: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 Spring Dragon appears to have rolled out a steady mix of exploits against government-related organizations in VN , TW , PH , and other locations over the past few years .' [/INST] [The:O, Spring:B-HackOrg, Dragon:I-HackOrg, appears:O, to:O, have:O, rolled:O, out:O, a:O, steady:O, mix:O, of:O, exploits:O, against:O, government-related:B-Org, organizations:I-Org, in:O, VN:B-Area, ,:O, TW:B-Area, ,:O, PH:B-Area, ,:O, and:O, other:O, locations:O, over:O, the:O, past:O, few:O, years: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: 'Organizations located in Myanmar and targeted by Spring Dragon have gone unmentioned .' [/INST] [Organizations:O, located:O, in:O, Myanmar:B-Area, and:O, targeted:O, by:O, Spring:B-HackOrg, Dragon:I-HackOrg, have:O, gone:O, unmentioned: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: 'Spring Dragon 's infiltration techniques there were not simply spearphish .' [/INST] [Spring:B-HackOrg, Dragon:I-HackOrg, 's:O, infiltration:O, techniques:O, there:O, were:O, not:O, simply:O, spearphish:B-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: 'The download name was , Zawgyi_Keyboard_L.zip , , and it dropped a , setup.exe , that contained several backdoor components , including an Elise , wincex.dll , ( a42c966e26f3577534d03248551232f3 , detected as Backdoor.Win32.Agent.delp ) .' [/INST] [The:O, download:O, name:O, was:O, ,:O, Zawgyi_Keyboard_L.zip:B-SamFile, ,:O, ,:O, and:O, it:O, dropped:O, a:O, ,:O, setup.exe:B-SamFile, ,:O, that:O, contained:O, several:O, backdoor:O, components:O, ,:O, including:O, an:O, Elise:B-Tool, ,:O, wincex.dll:B-SamFile, ,:O, (:O, a42c966e26f3577534d03248551232f3:O, ,:O, detected:O, as:O, Backdoor.Win32.Agent.delp:O, ):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: 'While this particular actor effectively used their almost worn out CVE-2012-0158 exploits in the past , Spring Dragon employs more involved and creative intrusive activity as well .' [/INST] [While:O, this:O, particular:O, actor:B-HackOrg, effectively:O, used:O, their:O, almost:O, worn:O, out:O, CVE-2012-0158:B-Exp, exploits:O, in:O, the:O, past:O, ,:O, Spring:B-HackOrg, Dragon:I-HackOrg, employs:O, more:O, involved:O, and:O, creative:O, intrusive:O, activity:B-OffAct, as:O, well: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 well-known threat group called DRAGONFISH or Lotus Blossom are distributing a new form of Elise malware targeting organizations for espionage purposes .' [/INST] [The:O, well-known:O, threat:B-HackOrg, group:I-HackOrg, called:O, DRAGONFISH:B-HackOrg, or:O, Lotus:B-HackOrg, Blossom:I-HackOrg, are:O, distributing:O, a:O, new:O, form:O, of:O, Elise:B-Tool, malware:I-Tool, targeting:O, organizations:O, for:O, espionage:B-HackOrg, purposes: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 threat actors associated with DRAGONFISH have previously focused their campaigns on targets in Southeast Asia , specifically those located in countries near the South China Sea .' [/INST] [The:O, threat:B-HackOrg, actors:I-HackOrg, associated:O, with:O, DRAGONFISH:B-HackOrg, have:O, previously:O, focused:O, their:O, campaigns:B-OffAct, on:O, targets:O, in:O, Southeast:B-Area, Asia:I-Area, ,:O, specifically:O, those:O, located:O, in:O, countries:O, near:O, the:O, South:B-Area, China:I-Area, Sea: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: 'iDefense analysts have identified a campaign likely to be targeting members of— or those with affiliation or interest in—the ASEAN Defence Ministers ' Meeting ( ADMM ) .' [/INST] [iDefense:B-SecTeam, analysts:O, have:O, identified:O, a:O, campaign:B-OffAct, likely:O, to:O, be:O, targeting:O, members:O, of—:O, or:O, those:O, with:O, affiliation:O, or:O, interest:O, in—the:O, ASEAN:O, Defence:B-Org, Ministers:I-Org, ':I-Org, Meeting:I-Org, (:O, ADMM:B-Org, ):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: 'iDefense analysts have identified a campaign likely to be targeting members of or those with affiliation or interest in the ASEAN Defence Minister 's Meeting ( ADMM ) .' [/INST] [iDefense:B-SecTeam, analysts:O, have:O, identified:O, a:O, campaign:B-OffAct, likely:O, to:O, be:O, targeting:O, members:O, of:O, or:O, those:O, with:O, affiliation:O, or:O, interest:O, in:O, the:O, ASEAN:B-Org, Defence:I-Org, Minister:I-Org, 's:I-Org, Meeting:I-Org, (:O, ADMM:B-Org, ):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: 'iDefense assesses with high confidence that this campaign is associated with the threat group DRAGONFISH ( also known as Lotus Blossom and Spring Dragon ) .' [/INST] [iDefense:B-SecTeam, assesses:O, with:O, high:O, confidence:O, that:O, this:O, campaign:B-OffAct, is:O, associated:O, with:O, the:O, threat:B-HackOrg, group:I-HackOrg, DRAGONFISH:B-HackOrg, (:O, also:O, known:O, as:O, Lotus:B-HackOrg, Blossom:I-HackOrg, and:O, Spring:B-HackOrg, Dragon:I-HackOrg, ):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 mitigate the threat of the described campaign , security teams can consider blocking access to the C2 server 103.236.150.14 and , where applicable , ensure that the Microsoft Security Update KB2553204 is installed in order to patch the CVE-2017-11882 vulnerability .' [/INST] [To:O, mitigate:O, the:O, threat:O, of:O, the:O, described:O, campaign:B-OffAct, ,:O, security:O, teams:O, can:O, consider:O, blocking:O, access:O, to:O, the:O, C2:O, server:O, 103.236.150.14:O, and:O, ,:O, where:O, applicable:O, ,:O, ensure:O, that:O, the:O, Microsoft:O, Security:O, Update:O, KB2553204:O, is:O, installed:O, in:O, order:O, to:O, patch:O, the:O, CVE-2017-11882:B-Exp, vulnerability: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 attempted to exploit CVE-2014-6332 using a slightly modified version of the proof-of-concept ( POC ) code to install a Trojan called Emissary , which is related to the Operation Lotus Blossom campaign .' [/INST] [The:O, actors:B-HackOrg, attempted:O, to:O, exploit:O, CVE-2014-6332:B-Exp, using:O, a:O, slightly:O, modified:O, version:O, of:O, the:O, proof-of-concept:O, (:O, POC:O, ):O, code:O, to:O, install:O, a:O, Trojan:O, called:O, Emissary:B-Tool, ,:O, which:O, is:O, related:O, to:O, the:O, Operation:B-OffAct, Lotus:I-OffAct, Blossom:I-OffAct, campaign:I-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: 'The targeting of this individual suggests the actors are interested in breaching the French Ministry of Foreign Affairs itself or gaining insights into relations between France and Taiwan .' [/INST] [The:O, targeting:O, of:O, this:O, individual:B-Org, suggests:O, the:O, actors:B-HackOrg, are:O, interested:O, in:O, breaching:O, the:O, French:O, Ministry:O, of:O, Foreign:O, Affairs:O, itself:O, or:O, gaining:B-Purp, insights:I-Purp, into:I-Purp, relations:I-Purp, between:O, France:B-Area, and:O, Taiwan: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: 'On November 10 , 2015 , threat actors sent a spear-phishing email to an individual at the French Ministry of Foreign Affairs .' [/INST] [On:O, November:B-Time, 10:I-Time, ,:I-Time, 2015:I-Time, ,:O, threat:B-HackOrg, actors:I-HackOrg, sent:O, a:O, spear-phishing:B-Way, email:I-Way, to:O, an:O, individual:B-Org, at:O, the:O, French:O, Ministry:O, of:O, Foreign:O, Affairs: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: 'On November 10 , 2015 , Lotus Blossom sent a spear-phishing email to an individual at the French Ministry of Foreign Affairs .' [/INST] [On:O, November:B-Time, 10:I-Time, ,:I-Time, 2015:I-Time, ,:O, Lotus:B-HackOrg, Blossom:I-HackOrg, sent:O, a:O, spear-phishing:B-Way, email:I-Way, to:O, an:O, individual:B-Org, at:O, the:O, French:O, Ministry:O, of:O, Foreign:O, Affairs: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: 'Both attachments are malicious Word documents that attempt to exploit the Windows OLE Automation Array Remote Code Execution Vulnerability tracked by CVE-2014-6332 .' [/INST] [Both:O, attachments:O, are:O, malicious:B-SamFile, Word:I-SamFile, documents:I-SamFile, that:O, attempt:O, to:O, exploit:O, the:O, Windows:B-Exp, OLE:I-Exp, Automation:I-Exp, Array:I-Exp, Remote:I-Exp, Code:I-Exp, Execution:I-Exp, Vulnerability:I-Exp, tracked:O, by:O, CVE-2014-6332: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: 'Lotus Blossom attempted to exploit CVE-2014-6332 using the POC code available in the wild .' [/INST] [Lotus:B-HackOrg, Blossom:I-HackOrg, attempted:O, to:O, exploit:O, CVE-2014-6332:B-Exp, using:O, the:O, POC:O, code:O, available:O, in:O, the:O, wild: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 Trojan is related to the Elise backdoor described in the Operation Lotus Blossom report .' [/INST] [This:O, Trojan:O, is:O, related:O, to:O, the:O, Elise:B-Tool, backdoor:I-Tool, described:O, in:O, the:O, Operation:B-OffAct, Lotus:I-OffAct, Blossom:I-OffAct, report: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: 'Lotus Blossom was attempting to exploit CVE-2014-6332 to install a new version of the Emissary Trojan , specifically version 5.3 .' [/INST] [Lotus:B-HackOrg, Blossom:I-HackOrg, was:O, attempting:O, to:O, exploit:O, CVE-2014-6332:B-Exp, to:O, install:O, a:O, new:O, version:O, of:O, the:O, Emissary:B-Tool, Trojan:I-Tool, ,:O, specifically:O, version:O, 5.3: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: 'APT threat actors , most likely nation state-sponsored , targeted a diplomat in the French Ministry of Foreign Affairs with a seemingly legitimate invitation to a technology conference in Taiwan .' [/INST] [APT:B-HackOrg, threat:I-HackOrg, actors:I-HackOrg, ,:O, most:O, likely:O, nation:O, state-sponsored:O, ,:O, targeted:O, a:O, diplomat:B-Org, in:O, the:O, French:O, Ministry:O, of:O, Foreign:O, Affairs:O, with:O, a:O, seemingly:O, legitimate:O, invitation:O, to:O, a:O, technology:O, conference:O, in:O, Taiwan: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: 'Additionally , the targeting of a French diplomat based in Taipei , Taiwan aligns with previous targeting by these actors , as does the separate infrastructure .' [/INST] [Additionally:O, ,:O, the:O, targeting:O, of:O, a:O, French:B-Org, diplomat:I-Org, based:O, in:O, Taipei:O, ,:O, Taiwan:B-Area, aligns:O, with:O, previous:O, targeting:O, by:O, these:O, actors:B-HackOrg, ,:O, as:O, does:O, the:O, separate: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 Elise malware used by Lotus Blossom , which was an attack campaign on targets in Southeast Asia .' [/INST] [The:O, Elise:B-Tool, malware:I-Tool, used:O, by:O, Lotus:B-HackOrg, Blossom:I-HackOrg, ,:O, which:O, was:O, an:O, attack:B-OffAct, campaign:I-OffAct, on:O, targets:O, in:O, Southeast:B-Area, Asia: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: 'Based on the targeting and lures , Unit 42 assesses that the Lotus Blossom actors ' collection requirements include militaries and government agencies in Southeast Asia .' [/INST] [Based:O, on:O, the:O, targeting:O, and:O, lures:O, ,:O, Unit:B-SecTeam, 42:I-SecTeam, assesses:O, that:O, the:O, Lotus:B-HackOrg, Blossom:I-HackOrg, actors:I-HackOrg, ':O, collection:O, requirements:O, include:O, militaries:B-Idus, and:O, government:B-Org, agencies:I-Org, in:O, Southeast:B-Area, Asia: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: 'In December 2015 , Unit 42 published a blog about a cyber espionage attack using the Emissary Trojan as a payload .' [/INST] [In:O, December:B-Time, 2015:I-Time, ,:O, Unit:B-SecTeam, 42:I-SecTeam, published:O, a:O, blog:O, about:O, a:O, cyber:B-OffAct, espionage:I-OffAct, attack:I-OffAct, using:O, the:O, Emissary:B-Tool, Trojan:I-Tool, as:O, a:O, payload: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 oldest sample we found was created in 2009 , indicating this tool has been in use for almost seven years .' [/INST] [The:O, oldest:O, sample:O, we:O, found:O, was:O, created:O, in:O, 2009:B-Time, ,:O, indicating:O, this:O, tool:O, has:O, been:O, in:O, use:O, for:O, almost:O, seven:O, years: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 addition , Emissary appears to against Taiwan or Hong Kong , all of the decoys are written in Traditional Chinese , and they use themes related to the government or military .' [/INST] [In:O, addition:O, ,:O, Emissary:B-Tool, appears:O, to:O, against:O, Taiwan:B-Area, or:O, Hong:B-Area, Kong:I-Area, ,:O, all:O, of:O, the:O, decoys:O, are:O, written:O, in:O, Traditional:O, Chinese:B-Area, ,:O, and:O, they:O, use:O, themes:O, related:O, to:O, the:O, government:B-Idus, or:O, military: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: 'Of note , this is three years earlier than the oldest Elise sample we have found , suggesting this group has been active longer than previously documented .' [/INST] [Of:O, note:O, ,:O, this:O, is:O, three:O, years:O, earlier:O, than:O, the:O, oldest:O, Elise:B-Tool, sample:I-Tool, we:O, have:O, found:O, ,:O, suggesting:O, this:O, group:B-HackOrg, has:O, been:O, active:O, longer:O, than:O, previously:O, documented: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 addition , we observed a TTP shift post publication with regards to their malware delivery ; they started using compromised but legitimate domains to serve their malware .' [/INST] [In:O, addition:O, ,:O, we:O, observed:O, a:O, TTP:O, shift:O, post:O, publication:O, with:O, regards:O, to:O, their:O, malware:O, delivery:O, ;:O, they:O, started:O, using:O, compromised:O, but:O, legitimate:B-Tool, domains:I-Tool, to:O, serve:O, their:O, malware: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: 'All of the Emissary we've collected are written in Traditional Chinese , which is used primarily in Taiwan and Hong Kong .' [/INST] [All:O, of:O, the:O, Emissary:B-Tool, we've:O, collected:O, are:O, written:O, in:O, Traditional:O, Chinese:B-Area, ,:O, which:O, is:O, used:O, primarily:O, in:O, Taiwan:B-Area, and:O, Hong:B-Area, Kong: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: 'One of the most interesting observations made during this analysis is that the amount of development effort devoted to Emissary significantly increased after we published our Operation Lotus Blossom report in June 2015 , resulting in many new versions of the Emissary Trojan .' [/INST] [One:O, of:O, the:O, most:O, interesting:O, observations:O, made:O, during:O, this:O, analysis:O, is:O, that:O, the:O, amount:O, of:O, development:O, effort:O, devoted:O, to:O, Emissary:B-Tool, significantly:O, increased:O, after:O, we:O, published:O, our:O, Operation:B-OffAct, Lotus:I-OffAct, Blossom:I-OffAct, report:O, in:O, June:B-Time, 2015:I-Time, ,:O, resulting:O, in:O, many:O, new:O, versions:O, of:O, the:O, Emissary:B-Tool, Trojan: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: 'Lotus Blossom targeted the government , higher education , and high tech companies .' [/INST] [Lotus:B-HackOrg, Blossom:I-HackOrg, targeted:O, the:O, government:B-Idus, ,:O, higher:B-Idus, education:I-Idus, ,:O, and:O, high:B-Org, tech:I-Org, companies: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: 'Our evidence suggests that malware authors created Emissary as early as 2009 , which suggests that threat actors have relied on this tool as a payload in cyber-espionage attacks for many years .' [/INST] [Our:O, evidence:O, suggests:O, that:O, malware:O, authors:O, created:O, Emissary:B-Tool, as:O, early:O, as:O, 2009:B-Time, ,:O, which:O, suggests:O, that:O, threat:B-HackOrg, actors:I-HackOrg, have:O, relied:O, on:O, this:O, tool:O, as:O, a:O, payload:O, in:O, cyber-espionage:B-OffAct, attacks:I-OffAct, for:O, many:O, years: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: 'While it lacks more advanced functionality like screen capturing , it is still able to carry out most tasks desired by threat actors : exfiltration of files , ability to download and execute additional payloads , and gain remote shell access .' [/INST] [While:O, it:O, lacks:O, more:O, advanced:O, functionality:O, like:O, screen:O, capturing:O, ,:O, it:O, is:O, still:O, able:O, to:O, carry:O, out:O, most:O, tasks:O, desired:O, by:O, threat:B-HackOrg, actors:I-HackOrg, ::O, exfiltration:B-Features, of:I-Features, files:I-Features, ,:O, ability:O, to:O, download:B-Features, and:O, execute:B-Features, additional:I-Features, payloads:I-Features, ,:O, and:O, gain:B-Features, remote:I-Features, shell:I-Features, access:I-Features, .: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 in Figure 2 shows that the Emissary Trojan was first created ( version 1.0 ) in May 2009 and quickly received an update that resulted in version 1.1 in June 2009 .' [/INST] [The:O, timeline:O, in:O, Figure:O, 2:O, shows:O, that:O, the:O, Emissary:B-Tool, Trojan:I-Tool, was:O, first:O, created:O, (:O, version:O, 1.0:O, ):O, in:O, May:B-Time, 2009:I-Time, and:O, quickly:O, received:O, an:O, update:O, that:O, resulted:O, in:O, version:O, 1.1:O, in:O, June:B-Time, 2009: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: 'Between August and November 2015 the malware author creates several new versions of Emissary , specifically 5.0 , 5.1 , 5.3 and 5.4 in a much more rapid succession compared to development process in earlier versions .' [/INST] [Between:O, August:B-Time, and:O, November:B-Time, 2015:I-Time, the:O, malware:O, author:O, creates:O, several:O, new:O, versions:O, of:O, Emissary:B-Tool, ,:O, specifically:O, 5.0:O, ,:O, 5.1:O, ,:O, 5.3:O, and:O, 5.4:O, in:O, a:O, much:O, more:O, rapid:O, succession:O, compared:O, to:O, development:O, process:O, in:O, earlier:O, versions: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: 'Version 2.0 received one update in October 2013 before the malware author released version 3.0 in December 2014 .' [/INST] [Version:O, 2.0:O, received:O, one:O, update:O, in:O, October:B-Time, 2013:I-Time, before:O, the:O, malware:O, author:O, released:O, version:O, 3.0:O, in:O, December:B-Time, 2014: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: 'While this may be coincidental , the out-of-sequence version 3.0 sample was created ten days after we published the Operation Lotus Blossom paper that exposed the Elise Trojan that is closely related to Emissary .' [/INST] [While:O, this:O, may:O, be:O, coincidental:O, ,:O, the:O, out-of-sequence:O, version:O, 3.0:O, sample:O, was:O, created:O, ten:O, days:O, after:O, we:O, published:O, the:O, Operation:B-OffAct, Lotus:I-OffAct, Blossom:I-OffAct, paper:O, that:O, exposed:O, the:O, Elise:B-Tool, Trojan:I-Tool, that:O, is:O, closely:O, related:O, to:O, Emissary:B-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 Lotus Blossom largely targets military or government , with some cases of higher education and high tech companies .' [/INST] [The:O, Lotus:B-HackOrg, Blossom:I-HackOrg, largely:O, targets:O, military:B-Idus, or:O, government:B-Idus, ,:O, with:O, some:O, cases:O, of:O, higher:B-Idus, education:I-Idus, and:O, high:B-Org, tech:I-Org, companies: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: 'The use of Emissary appears to be focused only on Taiwan and Hong Kong , with regular malware updates to avoid detection and to increase the odds of success .' [/INST] [The:O, use:O, of:O, Emissary:B-Tool, appears:O, to:O, be:O, focused:O, only:O, on:O, Taiwan:B-Area, and:O, Hong:B-Area, Kong:I-Area, ,:O, with:O, regular:O, malware:O, updates:O, to:O, avoid:O, detection:O, and:O, to:O, increase:O, the:O, odds:O, of:O, success: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 Lotus Blossom actors using Emissary have been active for at least seven years in Southeast Asia .' [/INST] [The:O, Lotus:B-HackOrg, Blossom:I-HackOrg, actors:I-HackOrg, using:O, Emissary:B-Tool, have:O, been:O, active:O, for:O, at:O, least:O, seven:O, years:O, in:O, Southeast:B-Area, Asia: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: 'Magic Hound has primarily targeted organizations in the energy , government , and technology sectors that are either based or have business interests in Saudi Arabia .' [/INST] [Magic:O, Hound:O, has:O, primarily:O, targeted:O, organizations:O, in:O, the:O, energy:B-Idus, ,:O, government:B-Idus, ,:O, and:O, technology:B-Org, sectors:I-Org, that:O, are:O, either:O, based:O, or:O, have:O, business:O, interests:O, in:O, Saudi:B-Area, Arabia: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: 'Regardless of causation , the rapid development of new versions of Emissary suggests that the malware authors are making frequent modifications to evade detection , which as a corollary suggests the Lotus Blossom are actively using the Emissary Trojan as a payload in attacks .' [/INST] [Regardless:O, of:O, causation:O, ,:O, the:O, rapid:O, development:O, of:O, new:O, versions:O, of:O, Emissary:B-Tool, suggests:O, that:O, the:O, malware:O, authors:O, are:O, making:O, frequent:O, modifications:O, to:O, evade:O, detection:O, ,:O, which:O, as:O, a:O, corollary:O, suggests:O, the:O, Lotus:B-HackOrg, Blossom:I-HackOrg, are:O, actively:O, using:O, the:O, Emissary:B-Tool, Trojan:I-Tool, as:O, a:O, payload:O, in:O, attacks: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: 'Link analysis of infrastructure and tools also revealed a potential relationship between Magic Hound and the adversary group called , Rocket Kitten , ( AKA Operation Saffron Rose , Ajax Security Team , Operation Woolen-Goldfish ) as well as an older attack campaign called Newscasters .' [/INST] [Link:O, analysis:O, of:O, infrastructure:O, and:O, tools:O, also:O, revealed:O, a:O, potential:O, relationship:O, between:O, Magic:O, Hound:O, and:O, the:O, adversary:O, group:B-HackOrg, called:O, ,:O, Rocket:B-HackOrg, Kitten:I-HackOrg, ,:O, (:O, AKA:O, Operation:B-HackOrg, Saffron:I-HackOrg, Rose:I-HackOrg, ,:O, Ajax:B-HackOrg, Security:I-HackOrg, Team:I-HackOrg, ,:O, Operation:B-HackOrg, Woolen-Goldfish:I-HackOrg, ):O, as:O, well:O, as:O, an:O, older:O, attack:B-OffAct, campaign:I-OffAct, called:O, Newscasters: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 addition to the malware evolution , the actors also shifted from solely spear-phishing targets with attachments to also compromising legitimate websites to host malware .' [/INST] [In:O, addition:O, to:O, the:O, malware:O, evolution:O, ,:O, the:O, actors:B-HackOrg, also:O, shifted:O, from:O, solely:O, spear-phishing:B-Way, targets:O, with:O, attachments:O, to:O, also:O, compromising:O, legitimate:O, websites:O, to:O, host:O, malware: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 is highly likely the Lotus Blossom used spear-phishing attacks containing links to these malicious documents as a delivery mechanism .' [/INST] [It:O, is:O, highly:O, likely:O, the:O, Lotus:B-HackOrg, Blossom:I-HackOrg, used:O, spear-phishing:B-OffAct, attacks:I-OffAct, containing:O, links:O, to:O, these:O, malicious:O, documents:O, as:O, a:O, delivery:O, mechanism: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 were ultimately able to identify multiple organizations in the government , energy , and technology sectors targeted by Magic Hound .' [/INST] [We:O, were:O, ultimately:O, able:O, to:O, identify:O, multiple:O, organizations:O, in:O, the:O, government:B-Idus, ,:O, energy:B-Idus, ,:O, and:O, technology:B-Org, sectors:I-Org, targeted:O, by:O, Magic:O, Hound: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 Magic Hound attacks did not rely on exploit code to compromise targeted systems , instead relying on Excel and Word documents containing malicious macros .' [/INST] [The:O, Magic:B-OffAct, Hound:I-OffAct, attacks:I-OffAct, did:O, not:O, rely:O, on:O, exploit:O, code:O, to:O, compromise:O, targeted:O, systems:O, ,:O, instead:O, relying:O, on:O, Excel:B-Way, and:O, Word:B-Way, documents:I-Way, containing:O, malicious:O, macros: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 MPK bot is not publicly available and had previously been attributed to an adversary group called , Rocket Kitten , which has often been thought to be a state sponsored adversary operating in the Middle East region .' [/INST] [The:O, MPK:B-Tool, bot:I-Tool, is:O, not:O, publicly:O, available:O, and:O, had:O, previously:O, been:O, attributed:O, to:O, an:O, adversary:O, group:B-HackOrg, called:O, ,:O, Rocket:B-HackOrg, Kitten:I-HackOrg, ,:O, which:O, has:O, often:O, been:O, thought:O, to:O, be:O, a:O, state:O, sponsored:O, adversary:O, operating:O, in:O, the:O, Middle:B-Area, East:I-Area, region: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 payload was a Python based open source remote administration tool ( RAT ) called Pupy .' [/INST] [One:O, payload:O, was:O, a:O, Python:O, based:O, open:O, source:O, remote:O, administration:O, tool:O, (:O, RAT:B-Tool, ):O, called:O, Pupy:B-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 Magic Hound campaign used Word and Excel documents containing malicious macros as a delivery method , specifically attempting to load MagicHound.Rollover .' [/INST] [The:O, Magic:B-OffAct, Hound:I-OffAct, campaign:I-OffAct, used:O, Word:B-Way, and:O, Excel:B-Way, documents:O, containing:O, malicious:O, macros:O, as:O, a:O, delivery:O, method:O, ,:O, specifically:O, attempting:O, to:O, load:O, MagicHound.Rollover:B-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: 'Many of the Fetch samples we analyzed attempted to obfuscate their functionality by encrypting their embedded strings using AES .' [/INST] [Many:O, of:O, the:O, Fetch:O, samples:O, we:O, analyzed:O, attempted:O, to:O, obfuscate:O, their:O, functionality:O, by:O, encrypting:O, their:O, embedded:O, strings:O, using:O, AES:B-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 loader 's main goal was to run a PowerShell command to execute shellcode .' [/INST] [The:O, loader:O, 's:O, main:O, goal:O, was:O, to:O, run:O, a:O, PowerShell:B-Tool, command:I-Tool, to:O, execute:O, shellcode: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 set up persistence , the loader writes a file to , c:\temp\rr.exe , and executes it with specific command line arguments to create auto run registry keys .' [/INST] [To:O, set:O, up:O, persistence:O, ,:O, the:O, loader:O, writes:O, a:O, file:O, to:O, ,:O, c:\temp\rr.exe:B-SamFile, ,:O, and:O, executes:O, it:O, with:O, specific:O, command:O, line:O, arguments:O, to:O, create:B-Features, auto:I-Features, run:I-Features, registry:I-Features, keys:I-Features, .: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 Magic Hound campaign was also discovered using a custom dropper tool , which we have named MagicHound.DropIt .' [/INST] [The:O, Magic:B-OffAct, Hound:I-OffAct, campaign:I-OffAct, was:O, also:O, discovered:O, using:O, a:O, custom:B-Tool, dropper:I-Tool, tool:O, ,:O, which:O, we:O, have:O, named:O, MagicHound.DropIt: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: 'We have also seen Magic Hound using DropIt as a binder , specifically dropping a legitimate decoy executable along with the malicious executable onto the target host .' [/INST] [We:O, have:O, also:O, seen:O, Magic:O, Hound:O, using:O, DropIt:B-Tool, as:O, a:O, binder:O, ,:O, specifically:O, dropping:O, a:O, legitimate:O, decoy:O, executable:O, along:O, with:O, the:O, malicious:O, executable:O, onto:O, the:O, target:O, host: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 found a second IRC bot called MPK using the same IP for its C2 server that a Leash sample was hosted on .' [/INST] [We:O, also:O, found:O, a:O, second:O, IRC:B-Tool, bot:I-Tool, called:O, MPK:B-Tool, using:O, the:O, same:O, IP:O, for:O, its:O, C2:O, server:O, that:O, a:O, Leash:B-Tool, sample:I-Tool, was:O, hosted:O, on: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 Magic Hound attack campaign is an active and persistent espionage motivated adversary operating in the Middle East region .' [/INST] [The:O, Magic:B-OffAct, Hound:I-OffAct, attack:I-OffAct, campaign:I-OffAct, is:O, an:O, active:O, and:O, persistent:O, espionage:B-HackOrg, motivated:O, adversary:O, operating:O, in:O, the:O, Middle:B-Area, East:I-Area, region: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: 'Organizations in the government , energy , and technology sectors have been targeted by Magic Hound , specifically organizations based in or doing business in Saudi Arabia .' [/INST] [Organizations:O, in:O, the:O, government:B-Idus, ,:O, energy:B-Idus, ,:O, and:O, technology:B-Org, sectors:I-Org, have:O, been:O, targeted:O, by:O, Magic:O, Hound:O, ,:O, specifically:O, organizations:O, based:O, in:O, or:O, doing:O, business:O, in:O, Saudi:B-Area, Arabia:I-Area, .:O] </s>