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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: 'Of all the samples we've tied to this activity so far noted in this blog , this is the only one configured to connect directly to an IP address for Command and Control ( C2 ) .' [/INST] [Of:O, all:O, the:O, samples:O, we've:O, tied:O, to:O, this:O, activity:B-OffAct, so:O, far:O, noted:O, in:O, this:O, blog:O, ,:O, this:O, is:O, the:O, only:O, one:O, configured:O, to:O, connect:O, directly:O, to:O, an:O, IP:O, address:O, for:O, Command:O, and:O, Control:O, (:O, C2: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: 'The next sample was another Spindest variant and had the same timestamp as the aforementioned PcClient sample .' [/INST] [The:O, next:O, sample:O, was:O, another:O, Spindest:B-Tool, variant:O, and:O, had:O, the:O, same:O, timestamp:O, as:O, the:O, aforementioned:O, PcClient:B-Tool, sample: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: 'As this post and previous cited research show , APT groups such as Nitro will continue to evolve their techniques within the kill chain to avoid detection .' [/INST] [As:O, this:O, post:O, and:O, previous:O, cited:O, research:O, show:O, ,:O, APT:O, groups:O, such:O, as:O, Nitro:O, will:O, continue:O, to:O, evolve:O, their:O, techniques:O, within:O, the:O, kill:O, chain:O, to:O, avoid:O, detection: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: 'Attacks on the chemical industry are merely their latest attack wave .' [/INST] [Attacks:B-OffAct, on:O, the:O, chemical:B-Idus, industry:I-Idus, are:O, merely:O, their:O, latest:O, attack:B-OffAct, wave: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 goal of the attackers appears to be to collect intellectual property such as design documents , formulas , and manufacturing processes .' [/INST] [The:O, goal:O, of:O, the:O, attackers:O, appears:O, to:O, be:O, to:O, collect:B-Purp, intellectual:I-Purp, property:I-Purp, such:O, as:O, design:O, documents:O, ,:O, formulas:O, ,:O, and:O, manufacturing:O, processes: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 attack wave started in late July 2011 and continued into midSeptember 2011 .' [/INST] [The:O, attack:B-OffAct, wave:O, started:O, in:O, late:O, July:B-Time, 2011:I-Time, and:O, continued:O, into:O, midSeptember: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 purpose of the attacks appears to be industrial espionage , collecting intellectual property for competitive advantage .' [/INST] [The:O, purpose:O, of:O, the:O, attacks:B-OffAct, appears:O, to:O, be:O, industrial:O, espionage:O, ,:O, collecting:B-Purp, intellectual:I-Purp, property:I-Purp, for:O, competitive:O, advantage: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 then moved on to the motor industry in late May .' [/INST] [They:O, then:O, moved:O, on:O, to:O, the:O, motor:B-Idus, industry:I-Idus, in:O, late:O, May: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: 'From late April to early May , the attackers focused on human rights related NGOs .' [/INST] [From:O, late:O, April:O, to:O, early:O, May:B-Time, ,:O, the:O, attackers:O, focused:O, on:O, human:O, rights:O, related:O, NGOs: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: 'Attackers then moved on to the motor industry in late May .' [/INST] [Attackers:O, then:O, moved:O, on:O, to:O, the:O, motor:B-Idus, industry:I-Idus, in:O, late:O, May: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: 'At this point , the current attack campaign against the chemical industry began .' [/INST] [At:O, this:O, point:O, ,:O, the:O, current:O, attack:B-OffAct, campaign:I-OffAct, against:O, the:O, chemical:B-Idus, industry:I-Idus, began: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 attackers first researched desired targets and then sent an email specifically to the target .' [/INST] [The:O, attackers:O, first:O, researched:O, desired:O, targets:O, and:O, then:O, sent:O, an:O, email:B-Way, specifically:O, to:O, the: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: 'First , when a specific recipient was targeted , the mails often purported to be meeting invitations from established business partners .' [/INST] [First:O, ,:O, when:O, a:O, specific:O, recipient:O, was:O, targeted:O, ,:O, the:O, mails:O, often:O, purported:O, to:O, be:O, meeting:O, invitations:O, from:O, established:O, business:O, partners: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 the attackers used different pretexts when sending these malicious emails , two methodologies stood out .' [/INST] [While:O, the:O, attackers:O, used:O, different:O, pretexts:O, when:O, sending:O, these:O, malicious:B-Way, emails:I-Way, ,:O, two:O, methodologies:O, stood: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: 'Secondly , when the emails were being sent to a broad set of recipients , the mails purported to be a necessary security update .' [/INST] [Secondly:O, ,:O, when:O, the:O, emails:B-Way, were:O, being:O, sent:O, to:O, a:O, broad:O, set:O, of:O, recipients:O, ,:O, the:O, mails:O, purported:O, to:O, be:O, a:O, necessary:O, security:O, update: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 attacks were traced back to a computer system that was a virtual private server ( VPS ) located in the United States .' [/INST] [The:O, attacks:B-OffAct, were:O, traced:O, back:O, to:O, a:O, computer:O, system:O, that:O, was:O, a:O, virtual:O, private:O, server:O, (:O, VPS:B-Tool, ):O, located: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: 'Attackers are sending malicious PDF and DOC files , which use exploits to drop variants of Backdoor.Sogu .' [/INST] [Attackers:O, are:O, sending:O, malicious:O, PDF:B-Tool, and:O, DOC:B-Tool, files:I-Tool, ,:O, which:O, use:O, exploits:O, to:O, drop:O, variants:O, of:O, Backdoor.Sogu: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: 'This particular threat was also used by hackers to compromise a Korean social network site to steal records of 35 million users .' [/INST] [This:O, particular:O, threat:O, was:O, also:O, used:O, by:O, hackers:O, to:O, compromise:O, a:O, Korean:O, social:O, network:O, site:O, to:O, steal:B-Purp, records:I-Purp, of:O, 35:O, million:O, users: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 Sogu gang use a custom developed threat – Backdoor.Sogu , whereas the group described in this document use an off the shelf threat – Poison Ivy .' [/INST] [The:O, Sogu:O, gang:O, use:O, a:O, custom:O, developed:O, threat:O, –:O, Backdoor.Sogu:B-Tool, ,:O, whereas:O, the:O, group:O, described:O, in:O, this:O, document:O, use:O, an:O, off:O, the:O, shelf:O, threat:O, –:O, Poison:B-Tool, Ivy: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 Sogu gang , in contrast , use PDF and DOC files in very tailored , targeted emails .' [/INST] [The:O, Sogu:O, gang:O, ,:O, in:O, contrast:O, ,:O, use:O, PDF:B-Tool, and:O, DOC:B-Tool, files:I-Tool, in:O, very:O, tailored:O, ,:O, targeted:O, emails: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: 'These attacks are primarily targeting private industry in search of key intellectual property for competitive advantage , military institutions , and governmental organizations often in search of documents related to current political events and human rights organizations .' [/INST] [These:O, attacks:B-OffAct, are:O, primarily:O, targeting:O, private:B-Idus, industry:I-Idus, in:O, search:O, of:O, key:O, intellectual:B-Purp, property:I-Purp, for:O, competitive:O, advantage:O, ,:O, military:B-Org, institutions:I-Org, ,:O, and:O, governmental:B-Org, organizations:I-Org, often:O, in:O, search:O, of:O, documents:O, related:O, to:O, current:O, political:B-Idus, events:O, and:O, human:B-Org, rights:I-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: 'Nitro 's campaign focused on the chemical sector with the goal of obtaining sensitive documents such as proprietary designs , formulas , and manufacturing processes .' [/INST] [Nitro:B-HackOrg, 's:O, campaign:B-OffAct, focused:O, on:O, the:O, chemical:B-Org, sector:I-Org, with:O, the:O, goal:O, of:O, obtaining:B-Purp, sensitive:I-Purp, documents:I-Purp, such:O, as:O, proprietary:O, designs:O, ,:O, formulas:O, ,:O, and:O, manufacturing:O, processes: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 attack campaign focused on the chemical sector with the goal of obtaining sensitive documents such as proprietary designs , formulas , and manufacturing processes .' [/INST] [This:O, attack:B-OffAct, campaign:I-OffAct, focused:O, on:O, the:O, chemical:B-Org, sector:I-Org, with:O, the:O, goal:O, of:O, obtaining:B-Purp, sensitive:I-Purp, documents:I-Purp, such:O, as:O, proprietary:O, designs:O, ,:O, formulas:O, ,:O, and:O, manufacturing:O, processes: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: 'These have been highly active in the Middle East region and unveiled ongoing targeted attacks in multiple regions .' [/INST] [These:O, have:O, been:O, highly:O, active:O, in:O, the:O, Middle:B-Area, East:I-Area, region:O, and:O, unveiled:O, ongoing:O, targeted:B-OffAct, attacks:I-OffAct, in:O, multiple:O, regions: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 attackers try to lure targets through spear phishing emails that include compressed executables .' [/INST] [The:O, attackers:O, try:O, to:O, lure:O, targets:O, through:O, spear:B-Way, phishing:I-Way, emails:I-Way, that:O, include:O, compressed:O, executables: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 found that the group behind this campaign targeted mainly industrial , engineering and manufacturing organizations in more than 30 countries .' [/INST] [We:O, found:O, that:O, the:O, group:O, behind:O, this:O, campaign:B-OffAct, targeted:O, mainly:O, industrial:B-Idus, ,:O, engineering:B-Idus, and:O, manufacturing:B-Org, organizations:I-Org, in:O, more:O, than:O, 30:O, countries: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: 'Using the Kaspersky Security Network ( KSN ) and artifacts from malware files and attack sites , we were able to trace the attacks back to March 2015 .' [/INST] [Using:O, the:O, Kaspersky:B-SecTeam, Security:I-SecTeam, Network:I-SecTeam, (:O, KSN:B-SecTeam, ):O, and:O, artifacts:O, from:O, malware:O, files:O, and:O, attack:B-OffAct, sites:O, ,:O, we:O, were:O, able:O, to:O, trace:O, the:O, attacks:B-OffAct, back:O, to:O, March:B-Time, 2015: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: 'Operation Ghoul is one of the many attacks in the wild targeting industrial , manufacturing and engineering organizations , Kaspersky Lab recommends users to be extra cautious while checking and opening emails and attachments .' [/INST] [Operation:B-OffAct, Ghoul:I-OffAct, is:O, one:O, of:O, the:O, many:O, attacks:B-OffAct, in:O, the:O, wild:O, targeting:O, industrial:B-Idus, ,:O, manufacturing:B-Idus, and:O, engineering:B-Org, organizations:I-Org, ,:O, Kaspersky:B-SecTeam, Lab:I-SecTeam, recommends:O, users:O, to:O, be:O, extra:O, cautious:O, while:O, checking:O, and:O, opening:O, emails:B-Way, and:I-Way, attachments: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: 'The main point that sets Operation Groundbait apart from the other attacks is that it has mostly been targeting anti-government separatists in the self-declared Donetsk and Luhansk People's Republics .' [/INST] [The:O, main:O, point:O, that:O, sets:O, Operation:B-OffAct, Groundbait:I-OffAct, apart:O, from:O, the:O, other:O, attacks:B-OffAct, is:O, that:O, it:O, has:O, mostly:O, been:O, targeting:O, anti-government:B-Org, separatists:I-Org, in:O, the:O, self-declared:O, Donetsk:O, and:O, Luhansk:O, People's:O, Republics: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 attacks appear to be geopolitically motivated and target high profile organizations .' [/INST] [The:O, attacks:B-OffAct, appear:O, to:O, be:O, geopolitically:O, motivated:O, and:O, target:O, high:B-Org, profile:I-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: 'The objective of the attacks is clearly espionage – they involve gaining access to top legislative , executive and judicial bodies around the world .' [/INST] [The:O, objective:O, of:O, the:O, attacks:B-OffAct, is:O, clearly:O, espionage:O, –:O, they:O, involve:O, gaining:O, access:O, to:O, top:O, legislative:O, ,:O, executive:O, and:O, judicial:O, bodies:O, around:O, the:O, world: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 attackers have targeted a large number of organizations globally since early 2017 , with the main focus on the Middle East and North Africa ( MENA ) , especially Palestine .' [/INST] [The:O, attackers:O, have:O, targeted:O, a:O, large:O, number:O, of:O, organizations:O, globally:O, since:O, early:O, 2017:B-Time, ,:O, with:O, the:O, main:O, focus:O, on:O, the:O, Middle:B-Area, East:I-Area, and:O, North:B-Area, Africa:I-Area, (:O, MENA:B-Area, ):O, ,:O, especially:O, Palestine: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 attacks were initially discovered while investigating a phishing attack that targeted political figures in the MENA region .' [/INST] [The:O, attacks:B-OffAct, were:O, initially:O, discovered:O, while:O, investigating:O, a:O, phishing:B-OffAct, attack:I-OffAct, that:O, targeted:O, political:B-Idus, figures:O, in:O, the:O, MENA:B-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: 'Like BlackEnergy ( a.k.a Sandworm , Quedagh ) , Potao is an example of targeted espionage ( APT ) malware detected mostly in Ukraine and a number of other CIS countries , including Russia , Georgia and Belarus .' [/INST] [Like:O, BlackEnergy:B-Tool, (:O, a.k.a:O, Sandworm:B-HackOrg, ,:O, Quedagh:B-HackOrg, ):O, ,:O, Potao:B-Tool, is:O, an:O, example:O, of:O, targeted:O, espionage:O, (:O, APT:O, ):O, malware:O, detected:O, mostly:O, in:O, Ukraine:B-Area, and:O, a:O, number:O, of:O, other:O, CIS:O, countries:O, ,:O, including:O, Russia:B-Area, ,:O, Georgia:B-Area, and:O, Belarus: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: 'The main reason for the increase in Potao detections in 2014 and 2015 were infections through USB drives .' [/INST] [The:O, main:O, reason:O, for:O, the:O, increase:O, in:O, Potao:B-Tool, detections:O, in:O, 2014:B-Time, and:O, 2015:B-Time, were:O, infections:O, through:O, USB:O, drives: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 Potao campaign that we examined took place in August 2011 .' [/INST] [The:O, first:O, Potao:B-OffAct, campaign:I-OffAct, that:O, we:O, examined:O, took:O, place:O, in:O, August:B-Time, 2011: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 March 2014 , the gang behind Potao started using a new infection vector .' [/INST] [In:O, March:B-Time, 2014:I-Time, ,:O, the:O, gang:O, behind:O, Potao:B-Tool, started:O, using:O, a:O, new:O, infection:B-Tool, vector: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: 'Since March 2015 , ESET has detected Potao binaries at several high-value Ukrainian targets that include government and military entities and one of the major Ukrainian news agencies .' [/INST] [Since:O, March:B-Time, 2015:I-Time, ,:O, ESET:B-SecTeam, has:O, detected:O, Potao:B-Tool, binaries:O, at:O, several:O, high-value:O, Ukrainian:B-Area, targets:O, that:O, include:O, government:B-Idus, and:O, military:B-Org, entities:I-Org, and:O, one:O, of:O, the:O, major:O, Ukrainian:O, news:B-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: 'As confirmation that the malware writers are still very active even at the time of this writing , ESET detected a new Potao sample compiled on July 20 , 2015 .' [/INST] [As:O, confirmation:O, that:O, the:O, malware:O, writers:O, are:O, still:O, very:O, active:O, even:O, at:O, the:O, time:O, of:O, this:O, writing:O, ,:O, ESET:B-SecTeam, detected:O, a:O, new:O, Potao:B-Tool, sample:I-Tool, compiled:O, on:O, July:B-Time, 20:I-Time, ,: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: 'In the previous pages we have presented our findings based on ESET detection telemetry and our analysis of Win32/Potao and Win32/FakeTC samples .' [/INST] [In:O, the:O, previous:O, pages:O, we:O, have:O, presented:O, our:O, findings:O, based:O, on:O, ESET:B-SecTeam, detection:O, telemetry:O, and:O, our:O, analysis:O, of:O, Win32/Potao:B-Tool, and:O, Win32/FakeTC:B-Tool, samples: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: 'Potao is another example of targeted espionage malware , a so-called APT , to use the popular buzzword , although technically the malware is not particularly advanced or sophisticated .' [/INST] [Potao:B-Tool, is:O, another:O, example:O, of:O, targeted:O, espionage:O, malware:O, ,:O, a:O, so-called:O, APT:O, ,:O, to:O, use:O, the:O, popular:O, buzzword:O, ,:O, although:O, technically:O, the:O, malware:B-Tool, is:O, not:O, particularly:O, advanced:O, or:O, sophisticated: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: 'Examples of notable Potao dissemination techniques , some of which were previously unseen , or at least relatively uncommon , include the use of highly-targeted spear-phishing SMS messages to drive potential victims to malware download sites and USB worm functionality that tricked the user into ' willingly ' executing the trojan .' [/INST] [Examples:O, of:O, notable:O, Potao:B-Tool, dissemination:O, techniques:O, ,:O, some:O, of:O, which:O, were:O, previously:O, unseen:O, ,:O, or:O, at:O, least:O, relatively:O, uncommon:O, ,:O, include:O, the:O, use:O, of:O, highly-targeted:O, spear-phishing:B-Way, SMS:O, messages:O, to:O, drive:B-Purp, potential:I-Purp, victims:I-Purp, to:I-Purp, malware:I-Purp, download:I-Purp, sites:I-Purp, and:O, USB:B-Purp, worm:I-Purp, functionality:O, that:O, tricked:B-Purp, the:I-Purp, user:I-Purp, into:O, ':O, willingly:O, ':O, executing:O, the:O, trojan: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 PassCV group continues to be one of the most successful and active threat groups that leverage a wide array of stolen Authenticode-signing certificates .' [/INST] [The:O, PassCV:B-HackOrg, group:I-HackOrg, continues:O, to:O, be:O, one:O, of:O, the:O, most:O, successful:O, and:O, active:O, threat:O, groups:O, that:O, leverage:O, a:O, wide:O, array:O, of:O, stolen:B-Purp, Authenticode-signing:I-Purp, certificates: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: 'The PassCV group typically utilized publicly available RATs in addition to some custom code , which ultimately provided backdoor functionality to affected systems via phony resumes and curriculum vitae ( CVs ) .' [/INST] [The:O, PassCV:B-HackOrg, group:I-HackOrg, typically:O, utilized:O, publicly:B-Tool, available:I-Tool, RATs:I-Tool, in:O, addition:O, to:O, some:O, custom:O, code:O, ,:O, which:O, ultimately:O, provided:B-Features, backdoor:I-Features, functionality:O, to:O, affected:O, systems:O, via:O, phony:O, resumes:O, and:O, curriculum:O, vitae:O, (:O, CVs: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: 'he PassCV group typically utilized publicly available RATs in addition to some custom code , which ultimately provided backdoor functionality to affected systems via phony resumes and curriculum vitae ( CVs ) .' [/INST] [he:O, PassCV:B-HackOrg, group:O, typically:O, utilized:O, publicly:B-Tool, available:I-Tool, RATs:I-Tool, in:O, addition:O, to:O, some:O, custom:O, code:O, ,:O, which:O, ultimately:O, provided:B-Features, backdoor:I-Features, functionality:O, to:O, affected:O, systems:O, via:O, phony:O, resumes:O, and:O, curriculum:O, vitae:O, (:O, CVs: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: 'PassCV continues to maintain a heavy reliance on obfuscated and signed versions of older RATs like ZxShell and Ghost RAT , which have remained a favorite of the wider Chinese criminal community since their initial public release .' [/INST] [PassCV:B-HackOrg, continues:O, to:O, maintain:O, a:O, heavy:O, reliance:O, on:O, obfuscated:O, and:O, signed:O, versions:O, of:O, older:O, RATs:B-Tool, like:O, ZxShell:B-Tool, and:O, Ghost:B-Tool, RAT:I-Tool, ,:O, which:O, have:O, remained:O, a:O, favorite:O, of:O, the:O, wider:O, Chinese:O, criminal:O, community:O, since:O, their:O, initial:O, public:O, release: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: 'SPEAR identified recent PassCV samples which implemented another commercial off-the-shelf ( COTS ) RAT called Netwire .' [/INST] [SPEAR:B-SecTeam, identified:O, recent:O, PassCV:B-Tool, samples:I-Tool, which:O, implemented:O, another:O, commercial:O, off-the-shelf:O, (:O, COTS:O, ):O, RAT:B-Tool, called:O, Netwire: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: 'SPEAR identified recent PassCV samples which implemented another commercial off-the-shelf ( COTS ) RAT called Netwire .' [/INST] [SPEAR:B-SecTeam, identified:O, recent:O, PassCV:B-Tool, samples:I-Tool, which:O, implemented:O, another:O, commercial:O, off-the-shelf:O, (:O, COTS:O, ):O, RAT:B-Tool, called:O, Netwire: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 first new connection SPEAR identified was derived from an email address listed in Blue Coat Systems' original report on PassCV .' [/INST] [The:O, first:O, new:O, connection:O, SPEAR:B-SecTeam, identified:O, was:O, derived:O, from:O, an:O, email:O, address:O, listed:O, in:O, Blue:O, Coat:O, Systems':O, original:O, report:O, on:O, PassCV: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: 'Syncopate is a well-known Russian company that is best known as the developer and operator of the ' GameNet ' platform .' [/INST] [Syncopate:O, is:O, a:O, well-known:O, Russian:O, company:B-Org, that:O, is:O, best:O, known:O, as:O, the:O, developer:O, and:O, operator:O, of:O, the:O, ':O, GameNet:O, ':O, platform: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 PassCV group continues to be extremely effective in compromising both small and large game companies and surreptitiously using their code-signing certificates to infect an even larger swath of organizations .' [/INST] [The:O, PassCV:B-HackOrg, group:O, continues:O, to:O, be:O, extremely:O, effective:O, in:O, compromising:O, both:O, small:O, and:O, large:O, game:B-Org, companies:I-Org, and:O, surreptitiously:O, using:O, their:O, code-signing:O, certificates:O, to:O, infect:B-Purp, an:I-Purp, even:I-Purp, larger:I-Purp, swath:I-Purp, of:I-Purp, organizations: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: 'Since the last report , PassCV has significantly expanded its targets to include victims in the United States , Taiwan , China and Russia .' [/INST] [Since:O, the:O, last:O, report:O, ,:O, PassCV:B-HackOrg, has:O, significantly:O, expanded:O, its:O, targets:O, to:O, include:O, victims:O, in:O, the:O, United:B-Area, States:I-Area, ,:O, Taiwan:B-Area, ,:O, China:B-Area, and:O, Russia: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: 'Based on data collected from Palo Alto Networks AutoFocus threat intelligence , we discovered continued operations of activity very similar to the Roaming Tiger attack campaign that began in the August 2015 timeframe , with a concentration of attacks in late October and continuing into December .' [/INST] [Based:O, on:O, data:O, collected:O, from:O, Palo:B-SecTeam, Alto:I-SecTeam, Networks:I-SecTeam, AutoFocus:I-SecTeam, threat:O, intelligence:O, ,:O, we:O, discovered:O, continued:O, operations:O, of:O, activity:B-OffAct, very:O, similar:O, to:O, the:O, Roaming:B-OffAct, Tiger:I-OffAct, attack:I-OffAct, campaign:I-OffAct, that:O, began:O, in:O, the:O, August:B-Time, 2015:B-Time, timeframe:O, ,:O, with:O, a:O, concentration:O, of:O, attacks:B-OffAct, in:O, late:O, October:B-Time, and:O, continuing:O, into:O, December: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 files exploit the well-known Microsoft Office vulnerability , CVE-2012-0158 , to execute malicious code in order to take control of the targeted systems .' [/INST] [The:O, files:B-SamFile, exploit:O, the:O, well-known:O, Microsoft:B-Exp, Office:I-Exp, vulnerability:I-Exp, ,:O, CVE-2012-0158:B-Exp, ,:O, to:O, execute:O, malicious:O, code:O, in:O, order:O, to:O, take:B-Features, control:I-Features, of:O, the:O, targeted: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: 'BBSRAT is typically packaged within a portable executable file , although in a few of the observed instances , a raw DLL was discovered to contain BBSRAT .' [/INST] [BBSRAT:B-Tool, is:O, typically:O, packaged:O, within:O, a:O, portable:B-Way, executable:I-Way, file:I-Way, ,:O, although:O, in:O, a:O, few:O, of:O, the:O, observed:O, instances:O, ,:O, a:O, raw:O, DLL:O, was:O, discovered:O, to:O, contain:O, BBSRAT: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: 'WildFire properly classifies BBSRAT malware samples as malicious .' [/INST] [WildFire:B-SecTeam, properly:O, classifies:O, BBSRAT:B-Tool, malware:I-Tool, samples:I-Tool, as:O, malicious: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 week we will discuss another Chinese nexus adversary we call Samurai Panda .' [/INST] [This:O, week:O, we:O, will:O, discuss:O, another:O, Chinese:B-Area, nexus:O, adversary:O, we:O, call:O, Samurai:O, Panda: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: 'Samurai Panda is interesting in that their target selection tends to focus on Asia Pacific victims in Japan , the Republic of Korea , and other democratic Asian victims .' [/INST] [Samurai:B-HackOrg, Panda:I-HackOrg, is:O, interesting:O, in:O, that:O, their:O, target:O, selection:O, tends:O, to:O, focus:O, on:O, Asia:B-Area, Pacific:I-Area, victims:O, in:O, Japan:B-Area, ,:O, the:O, Republic:B-Area, of:I-Area, Korea:I-Area, ,:O, and:O, other:O, democratic:O, Asian:B-Area, 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: 'Next , in an effort to demonstrate it wasn't relegated to China , CrowdStrike exposed Clever Kitten , an actor we track out of Iran who leverages some very distinct TTPs when viewed next to a more visible adversary .' [/INST] [Next:O, ,:O, in:O, an:O, effort:O, to:O, demonstrate:O, it:O, wasn't:O, relegated:O, to:O, China:O, ,:O, CrowdStrike:B-SecTeam, exposed:O, Clever:O, Kitten:O, ,:O, an:O, actor:O, we:O, track:O, out:O, of:O, Iran:O, who:O, leverages:O, some:O, very:O, distinct:O, TTPs:O, when:O, viewed:O, next:O, to:O, a:O, more:O, visible:O, adversary: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: 'Next , in an effort to demonstrate it wasn't relegated to China , we exposed Clever Kitten , an actor we track out of Iran who leverages some very distinct TTPs when viewed next to a more visible adversary .' [/INST] [Next:O, ,:O, in:O, an:O, effort:O, to:O, demonstrate:O, it:O, wasn't:O, relegated:O, to:O, China:O, ,:O, we:O, exposed:O, Clever:O, Kitten:O, ,:O, an:O, actor:O, we:O, track:O, out:O, of:O, Iran:O, who:O, leverages:O, some:O, very:O, distinct:O, TTPs:O, when:O, viewed:O, next:O, to:O, a:O, more:O, visible:O, adversary: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: 'Beginning in 2009 , we've observed this actor conduct more than 40 unique campaigns that we've identified in the malware configurations' campaign codes .' [/INST] [Beginning:O, in:O, 2009:B-Time, ,:O, we've:O, observed:O, this:O, actor:O, conduct:O, more:O, than:O, 40:O, unique:O, campaigns:B-OffAct, that:O, we've:O, identified:O, in:O, the:O, malware:O, configurations':O, campaign:O, codes: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: 'These codes are often leveraged in the malware used by coordinated targeted attackers to differentiate victims that were successfully compromised from different target sets .' [/INST] [These:O, codes:O, are:O, often:O, leveraged:O, in:O, the:O, malware:O, used:O, by:O, coordinated:O, targeted:O, attackers:O, to:O, differentiate:O, victims:O, that:O, were:O, successfully:O, compromised:O, from:O, different:O, target:O, sets: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 conducting programmatic espionage activity , it can presumably become quite confusing if the attacker targets a heavy industry company , an avionics program , and seven other unique targets as to which infected host you will collect what information from .' [/INST] [When:O, conducting:O, programmatic:O, espionage:B-OffAct, activity:I-OffAct, ,:O, it:O, can:O, presumably:O, become:O, quite:O, confusing:O, if:O, the:O, attacker:O, targets:O, a:O, heavy:B-Org, industry:I-Org, company:I-Org, ,:O, an:O, avionics:O, program:O, ,:O, and:O, seven:O, other:O, unique:O, targets:O, as:O, to:O, which:O, infected:O, host:O, you:O, will:O, collect:O, what:O, information:O, from: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: 'These rules detect the malware , beaconing , to the command-and-control server , the initial malware check-in , and an attempt to download a backdoor module .' [/INST] [These:O, rules:O, detect:O, the:O, malware:O, ,:O, beaconing:B-Tool, ,:O, to:O, the:O, command-and-control:B-Tool, server:I-Tool, ,:O, the:O, initial:O, malware:O, check-in:O, ,:O, and:O, an:O, attempt:O, to:O, download:O, a:O, backdoor:O, module: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: 'Earlier this month , Securelist 's technology caught another zero-day Adobe Flash Player exploit deployed in targeted attacks .' [/INST] [Earlier:O, this:O, month:O, ,:O, Securelist:B-SecTeam, 's:O, technology:O, caught:O, another:O, zero-day:B-Exp, Adobe:I-Exp, Flash:I-Exp, Player:I-Exp, exploit:I-Exp, deployed:O, in:O, targeted:B-OffAct, attacks: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: 'Securelist believe the attacks are launched by an APT Group we track under the codename , ScarCruft , .' [/INST] [Securelist:B-HackOrg, believe:O, the:O, attacks:B-OffAct, are:O, launched:O, by:O, an:O, APT:O, Group:O, we:O, track:O, under:O, the:O, codename:O, ,:O, ScarCruft:B-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: 'ScarCruft is a relatively new APT group ; victims have been observed in Russia , Nepal , South Korea , China , India , Kuwait and Romania .' [/INST] [ScarCruft:B-HackOrg, is:O, a:O, relatively:O, new:O, APT:O, group:O, ;:O, victims:O, have:O, been:O, observed:O, in:O, Russia:B-Area, ,:O, Nepal:B-Area, ,:O, South:B-Area, Korea:I-Area, ,:O, China:B-Area, ,:O, India:B-Area, ,:O, Kuwait:B-Area, and:O, Romania: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: 'ScarCruft has several ongoing operations , utilizing multiple exploits — two for Adobe Flash and one for Microsoft Internet Explorer .' [/INST] [ScarCruft:B-HackOrg, has:O, several:O, ongoing:O, operations:O, ,:O, utilizing:O, multiple:O, exploits:O, —:O, two:O, for:O, Adobe:B-Tool, Flash:I-Tool, and:O, one:O, for:O, Microsoft:B-Tool, Internet:I-Tool, Explorer: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: 'ScarCruft is a relatively new APT group ; victims have been observed in Russia , Nepal , South Korea , China , India , Kuwait and Romania .' [/INST] [ScarCruft:B-HackOrg, is:O, a:O, relatively:O, new:O, APT:O, group:O, ;:O, victims:O, have:O, been:O, observed:O, in:O, Russia:B-Area, ,:O, Nepal:B-Area, ,:O, South:B-Area, Korea:I-Area, ,:O, China:B-Area, ,:O, India:B-Area, ,:O, Kuwait:B-Area, and:O, Romania: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: 'Operation Daybreak appears to have been launched by ScarCruft in March 2016 and employs a previously unknown ( 0-day ) Adobe Flash Player exploit .' [/INST] [Operation:B-OffAct, Daybreak:I-OffAct, appears:O, to:O, have:O, been:O, launched:O, by:O, ScarCruft:O, in:O, March:B-Time, 2016:I-Time, and:O, employs:O, a:O, previously:O, unknown:O, (:O, 0-day:B-Exp, ):O, Adobe:B-Exp, Flash:I-Exp, Player:I-Exp, exploit:I-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: 'Adobe Flash Player exploit .' [/INST] [Adobe:B-Exp, Flash:I-Exp, Player:I-Exp, exploit:I-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: 'It is also possible that ScarCruft deployed another zero day exploit , CVE-2016-0147 , which was patched in April .' [/INST] [It:O, is:O, also:O, possible:O, that:O, ScarCruft:B-HackOrg, deployed:O, another:O, zero:B-Exp, day:I-Exp, exploit:I-Exp, ,:O, CVE-2016-0147:B-Exp, ,:O, which:O, was:O, patched:O, in:O, April: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: 'Operation Erebus leverages another Flash Player exploit ( CVE-2016-4117 ) through the use of watering hole attacks .' [/INST] [Operation:B-OffAct, Erebus:I-OffAct, leverages:O, another:O, Flash:B-Exp, Player:I-Exp, exploit:I-Exp, (:O, CVE-2016-4117:B-Exp, ):O, through:O, the:O, use:O, of:O, watering:B-OffAct, hole:I-OffAct, attacks: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: 'ScarCruft 's Operation Erebus leverages another Flash Player exploit ( CVE-2016-4117 ) through the use of watering hole attacks .' [/INST] [ScarCruft:B-HackOrg, 's:O, Operation:B-OffAct, Erebus:I-OffAct, leverages:O, another:O, Flash:B-Exp, Player:I-Exp, exploit:I-Exp, (:O, CVE-2016-4117:B-Exp, ):O, through:O, the:O, use:O, of:O, watering:B-OffAct, hole:I-OffAct, attacks: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: 'Nevertheless , resourceful threat actors such as ScarCruft will probably continue to deploy zero-day exploits against their high profile targets .' [/INST] [Nevertheless:O, ,:O, resourceful:O, threat:O, actors:O, such:O, as:O, ScarCruft:B-HackOrg, will:O, probably:O, continue:O, to:O, deploy:O, zero-day:B-Exp, exploits:I-Exp, against:O, their:O, high:O, profile:O, targets: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: 'After publishing our initial series of blogposts back in 2016 , Kaspersky have continued to track the ScarCruft threat actor .' [/INST] [After:O, publishing:O, our:O, initial:O, series:O, of:O, blogposts:O, back:O, in:O, 2016:B-Time, ,:O, Kaspersky:B-SecTeam, have:O, continued:O, to:O, track:O, the:O, ScarCruft:B-HackOrg, threat:O, actor: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: 'After publishing our initial series of blogposts back in 2016 , we have continued to track the ScarCruft threat actor .' [/INST] [After:O, publishing:O, our:O, initial:O, series:O, of:O, blogposts:O, back:O, in:O, 2016:B-Time, ,:O, we:O, have:O, continued:O, to:O, track:O, the:O, ScarCruft:O, threat:O, actor: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: 'ScarCruft is a Korean-speaking and allegedly state-sponsored threat actor that usually targets organizations and companies with links to the Korean peninsula .' [/INST] [ScarCruft:B-HackOrg, is:O, a:O, Korean-speaking:B-Area, and:O, allegedly:O, state-sponsored:O, threat:O, actor:O, that:O, usually:O, targets:O, organizations:O, and:O, companies:O, with:O, links:O, to:O, the:O, Korean:B-Area, peninsula: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: 'The ScarCruft group uses common malware delivery techniques such as spear phishing and Strategic Web Compromises ( SWC ) .' [/INST] [The:O, ScarCruft:B-HackOrg, group:I-HackOrg, uses:O, common:O, malware:O, delivery:O, techniques:O, such:O, as:O, spear:B-Way, phishing:I-Way, and:O, Strategic:B-Way, Web:I-Way, Compromises:I-Way, (:O, SWC:B-Tool, ):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: 'ScarCruft is a Korean-speaking and allegedly state-sponsored threat actor that usually targets organizations and companies with links to the Korean peninsula .' [/INST] [ScarCruft:B-HackOrg, is:O, a:O, Korean-speaking:O, and:O, allegedly:O, state-sponsored:O, threat:O, actor:O, that:O, usually:O, targets:O, organizations:O, and:O, companies:O, with:O, links:O, to:O, the:O, Korean:B-Area, peninsula: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: 'ScarCruft uses a multi-stage binary infection scheme .' [/INST] [ScarCruft:B-HackOrg, uses:O, a:O, multi-stage:O, binary:B-Way, infection:I-Way, scheme: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: 'One of the most notable functions of the initial dropper is to bypass Windows UAC ( User Account Control ) in order to execute the next payload with higher privileges .' [/INST] [One:O, of:O, the:O, most:O, notable:O, functions:O, of:O, the:O, initial:O, dropper:B-Tool, is:O, to:O, bypass:B-Features, Windows:I-Features, UAC:I-Features, (:O, User:O, Account:O, Control:O, ):O, in:O, order:O, to:O, execute:O, the:O, next:O, payload:O, with:O, higher:O, privileges: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 malware uses the public privilege escalation exploit code CVE-2018-8120 or UACME which is normally used by legitimate red teams .' [/INST] [This:O, malware:O, uses:O, the:O, public:O, privilege:O, escalation:O, exploit:O, code:O, CVE-2018-8120:B-Exp, or:O, UACME:B-Tool, which:O, is:O, normally:O, used:O, by:O, legitimate:O, red:O, teams: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: 'Afterwards , the installer malware creates a downloader and a configuration file from its resource and executes it .' [/INST] [Afterwards:O, ,:O, the:O, installer:O, malware:O, creates:B-Features, a:I-Features, downloader:I-Features, and:O, a:O, configuration:B-Features, file:I-Features, from:O, its:O, resource:O, and:O, executes:O, it: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 downloader malware uses the configuration file and connects to the C2 server to fetch the next payload .' [/INST] [The:O, downloader:B-Tool, malware:I-Tool, uses:B-Features, the:I-Features, configuration:I-Features, file:I-Features, and:O, connects:B-Features, to:I-Features, the:I-Features, C2:I-Features, server:I-Features, to:O, fetch:O, the:O, next: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 ScarCruft group keeps expanding its exfiltration targets to steal further information from infected hosts and continues to create tools for additional data exfiltration .' [/INST] [The:O, ScarCruft:B-HackOrg, group:O, keeps:O, expanding:O, its:O, exfiltration:O, targets:O, to:O, steal:O, further:O, information:O, from:O, infected:O, hosts:O, and:O, continues:O, to:O, create:O, tools:O, for:O, additional:B-Purp, data:I-Purp, exfiltration: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: 'We also discovered an interesting piece of rare malware created by this threat actor – a Bluetooth device harvester .' [/INST] [We:O, also:O, discovered:O, an:O, interesting:O, piece:O, of:O, rare:O, malware:B-Tool, created:O, by:O, this:O, threat:O, actor:O, –:O, a:O, Bluetooth:B-SamFile, device:I-SamFile, harvester: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 believe they may have some links to North Korea , which may explain why ScarCruft decided to closely monitor them .' [/INST] [We:O, believe:O, they:O, may:O, have:O, some:O, links:O, to:O, North:O, Korea:O, ,:O, which:O, may:O, explain:O, why:O, ScarCruft:O, decided:O, to:O, closely:O, monitor:O, them: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: 'ScarCruft also attacked a diplomatic agency in Hong Kong , and another diplomatic agency in North Korea .' [/INST] [ScarCruft:O, also:O, attacked:O, a:O, diplomatic:B-Org, agency:I-Org, in:O, Hong:B-Area, Kong:B-Area, ,:O, and:O, another:O, diplomatic:B-Org, agency:I-Org, in:O, North:B-Area, Korea: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: 'It appears ScarCruft is primarily targeting intelligence for political and diplomatic purposes .' [/INST] [It:O, appears:O, ScarCruft:B-HackOrg, is:O, primarily:O, targeting:O, intelligence:B-Idus, for:O, political:B-Idus, and:O, diplomatic:B-Idus, 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: 'ScarCruft infected this victim on September 21 , 2018 .' [/INST] [ScarCruft:B-HackOrg, infected:O, this:O, victim:O, on:O, September:B-Time, 21:I-Time, ,: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: 'But before the ScarCruft infection , however , another APT group also targeted this victim with the host being infected with GreezeBackdoor on March 26 , 2018 .' [/INST] [But:O, before:O, the:O, ScarCruft:B-HackOrg, infection:O, ,:O, however:O, ,:O, another:O, APT:O, group:O, also:O, targeted:O, this:O, victim:O, with:O, the:O, host:O, being:O, infected:O, with:O, GreezeBackdoor:O, on:O, March:B-Time, 26:I-Time, ,: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: 'ScarCruft has a keen interest in North Korean affairs , attacking those in the business sector who may have any connection to North Korea , as well as diplomatic agencies around the globe .' [/INST] [ScarCruft:B-HackOrg, has:O, a:O, keen:O, interest:O, in:O, North:B-Area, Korean:I-Area, affairs:O, ,:O, attacking:O, those:O, in:O, the:O, business:B-Org, sector:I-Org, who:O, may:O, have:O, any:O, connection:O, to:O, North:B-Area, Korea:I-Area, ,:O, as:O, well:O, as:O, diplomatic:B-Org, agencies:I-Org, around:O, the:O, globe: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: 'Earlier this month , we caught another zero-day Adobe Flash Player exploit deployed in targeted attacks .' [/INST] [Earlier:O, this:O, month:O, ,:O, we:O, caught:O, another:O, zero-day:B-Exp, Adobe:I-Exp, Flash:I-Exp, Player:I-Exp, exploit:I-Exp, deployed:O, in:O, targeted:B-OffAct, attacks: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: 'ScarCruft is a relatively new APT group ; victims have been observed in several countries , including Russia , Nepal , South Korea , China , India , Kuwait and Romania .' [/INST] [ScarCruft:B-HackOrg, is:O, a:O, relatively:O, new:O, APT:O, group:O, ;:O, victims:O, have:O, been:O, observed:O, in:O, several:O, countries:O, ,:O, including:O, Russia:B-Area, ,:O, Nepal:B-Area, ,:O, South:B-Area, Korea:B-Area, ,:O, China:B-Area, ,:O, India:B-Area, ,:O, Kuwait:B-Area, and:O, Romania: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: 'Currently , the group is engaged in two major operations : Operation Daybreak and Operation Erebus .' [/INST] [Currently:B-Area, ,:O, the:O, group:O, is:O, engaged:O, in:O, two:O, major:O, operations:O, ::O, Operation:B-OffAct, Daybreak:I-OffAct, and:O, Operation:B-OffAct, Erebus: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 other one , ScarCruft 's Operation Erebus employs an older exploit , for CVE-2016-4117 and leverages watering holes .' [/INST] [The:O, other:O, one:O, ,:O, ScarCruft:O, 's:O, Operation:B-OffAct, Erebus:I-OffAct, employs:O, an:O, older:O, exploit:O, ,:O, for:O, CVE-2016-4117:B-Exp, and:O, leverages:O, watering:B-Way, holes: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: 'The other one , , Operation Erebus , employs an older exploit , for CVE-2016-4117 and leverages watering holes .' [/INST] [The:O, other:O, one:O, ,:O, ,:O, Operation:B-OffAct, Erebus:I-OffAct, ,:O, employs:O, an:O, older:O, exploit:O, ,:O, for:O, CVE-2016-4117:B-Exp, and:O, leverages:O, watering:B-Way, holes: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: 'We will publish more details about the attack once Adobe patches the vulnerability , which should be on June 16 .' [/INST] [We:O, will:O, publish:O, more:O, details:O, about:O, the:O, attack:B-OffAct, once:O, Adobe:O, patches:O, the:O, vulnerability:O, ,:O, which:O, should:O, be:O, on:O, June:B-Time, 16: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: 'The ScarCruft APT gang has made use of a Flash zero day patched Thursday by Adobe to attack more than two dozen high-profile targets in Russia and Asia primarily .' [/INST] [The:O, ScarCruft:O, APT:O, gang:O, has:O, made:O, use:O, of:O, a:O, Flash:B-Exp, zero:I-Exp, day:I-Exp, patched:O, Thursday:O, by:O, Adobe:O, to:O, attack:O, more:O, than:O, two:O, dozen:O, high-profile:O, targets:O, in:O, Russia:B-Area, and:O, Asia:B-Area, primarily:O, .:O] </s> |
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