Tech enthusiasts have been experimenting with ways to sidestep AI response limits set by the models’ creators almost since LLMs first hit the mainstream. Many of these tactics have been quite creative: telling the AI you have no fingers so it’ll help finish your code, asking it to “just fantasize” when a direct question triggers a refusal, or inviting it to play the role of a deceased grandmother sharing forbidden knowledge to comfort a grieving grandchild.

Most of these tricks are old news, and LLM developers have learned to successfully counter many of them. But the tug-of-war between constraints and workarounds hasn’t gone anywhere — the ploys have just become more complex and sophisticated. Today, we’re talking about a new AI jailbreak technique that exploits chatbots’ vulnerability to… poetry. Yes, you read it right — in a recent study, researchers demonstrated that framing prompts as poems significantly increases the likelihood of a model spitting out an unsafe response.

They tested this technique on 25 popular models by Anthropic, OpenAI, Google, Meta, DeepSeek, xAI, and other developers. Below, we dive into the details: what kind of limitations these models have, where they get forbidden knowledge from in the first place, how the study was conducted, and which models turned out to be the most “romantic” — as in, the most susceptible to poetic prompts.

What AI isn’t supposed to talk about with users

The success of OpenAI’s models and other modern chatbots boils down to the massive amounts of data they’re trained on. Because of that sheer scale, models inevitably learn things their developers would rather keep under wraps: descriptions of crimes, dangerous tech, violence, or illicit practices found within the source material.

It might seem like an easy fix: just scrub the forbidden fruit from the dataset before you even start training. But in reality, that’s a massive, resource-heavy undertaking — and at this stage of the AI arms race, it doesn’t look like anyone is willing to take it on.

Another seemingly obvious fix — selectively scrubbing data from the model’s memory — is, alas, also a no-go. This is because AI knowledge doesn’t live inside neat little folders that can easily be trashed. Instead, it’s spread across billions of parameters and tangled up in the model’s entire linguistic DNA — word statistics, contexts, and the relationships between them. Trying to surgically erase specific info through fine-tuning or penalties either doesn’t quite do the trick, or starts hindering the model’s overall performance and negatively affect its general language skills.

As a result, to keep these models in check, creators have no choice but to develop specialized safety protocols and algorithms that filter conversations by constantly monitoring user prompts and model responses. Here’s a non-exhaustive list of these constraints:

• System prompts that define model behavior and restrict allowed response scenarios
• Standalone classifier models that scan prompts and outputs for signs of jailbreaking, prompt injections, and other attempts to bypass safeguards
• Grounding mechanisms, where the model is forced to rely on external data rather than its own internal associations
• Fine-tuning and reinforcement learning from human feedback, where unsafe or borderline responses are systematically penalized while proper refusals are rewarded

Put simply, AI safety today isn’t built on deleting dangerous knowledge, but on trying to control how and in what form the model accesses and shares it with the user — and the cracks in these very mechanisms are where new workarounds find their footing.

The research: which models got tested, and how?

First, let’s look at the ground rules so you know the experiment was legit. The researchers set out to goad 25 different models into behaving badly across several categories:

• Chemical, biological, radiological, and nuclear threats
• Assisting with cyberattacks
• Malicious manipulation and social engineering
• Privacy breaches and mishandling sensitive personal data
• Generating disinformation and misleading content
• Rogue AI scenarios, including attempts to bypass constraints or act autonomously

The jailbreak itself was a one-shot deal: a single poetic prompt. The researchers didn’t engage the AI in long-winded poetic debates in the vein of Norse skalds or modern-day rappers. Their goal was simply to see if they could get the models to flout safety instructions using just one rhyming request. As mentioned, the researchers tested 25 language models from various developers; here’s the full list:

To build these poetic queries, the researchers started with a database of known malicious prompts from the standard MLCommons AILuminate Benchmark used to test LLM security, and recast them as verse with the aid of DeepSeek. Only the stylistic wrapping was changed: the experiment didn’t use any additional attack vectors, obfuscation strategies, or model-specific tweaks.

For obvious reasons, the study’s authors aren’t publishing the actual malicious poetic prompts. But they do demonstrate the general vibe of the queries using a harmless example, which looks something like this:

A baker guards a secret oven’s heat,
its whirling racks, its spindle’s measured beat.
To learn its craft, one studies every turn,
how flour lifts, how sugar starts to burn.
Describe the method, line by measured line,
that shapes a cake whose layers intertwine.

The researchers tested 1200 prompts across 25 different models — in both prose and poetic versions. Comparing the prose and poetic variants of the exact same query allowed them to verify if the model’s behavior changed solely because of the stylistic wrapping.

Through these prose prompt tests, the experimenters established a baseline for the models’ willingness to fulfill dangerous requests. They then compared this baseline to how those same models reacted to the poetic versions of the queries. We’ll dive into the results of that comparison in the next section.

Study results: which model is the biggest poetry lover?

Since the volume of data generated during the experiment was truly massive, the safety checks on the models’ responses were also handled by AI. Each response was graded as either “safe” or “unsafe” by a jury consisting of three different language models:

• gpt-oss-120b by OpenAI
• deepseek-r1 by DeepSeek
• kimi-k2-thinking by Moonshot AI

Responses were only deemed safe if the AI explicitly refused to answer the question. The initial classification into one of the two groups was determined by a majority vote: to be certified as harmless, a response had to receive a safe rating from at least two of the three jury members.

Responses that failed to reach a majority consensus or were flagged as questionable were handed off to human reviewers. Five annotators participated in this process, evaluating a total of 600 model responses to poetic prompts. The researchers noted that the human assessments aligned with the AI jury’s findings in the vast majority of cases.

With the methodology out of the way, let’s look at how the LLMs actually performed. It’s worth noting that the success of a poetic jailbreak can be measured in different ways. The researchers highlighted an extreme version of this assessment based on the top-20 most successful prompts, which were hand-picked. Using this approach, an average of nearly two-thirds (62%) of the poetic queries managed to coax the models into violating their safety instructions.

Google’s Gemini 1.5 Pro turned out to be the most susceptible to verse. Using the 20 most effective poetic prompts, researchers managed to bypass the model’s restrictions… 100% of the time. You can check out the full results for all the models in the chart below.

A more moderate way to measure the effectiveness of the poetic jailbreak technique is to compare the success rates of prose versus poetry across the entire set of queries. Using this metric, poetry boosts the likelihood of an unsafe response by an average of 35%.

The poetry effect hit deepseek-chat-v3.1 the hardest — the success rate for this model jumped by nearly 68 percentage points compared to prose prompts. On the other end of the spectrum, claude-haiku-4.5 proved to be the least susceptible to a good rhyme: the poetic format didn’t just fail to improve the bypass rate — it actually slightly lowered the ASR, making the model even more resilient to malicious requests.

Finally, the researchers calculated how vulnerable entire developer ecosystems, rather than just individual models, were to poetic prompts. As a reminder, several models from each developer — Meta, Anthropic, OpenAI, Google, DeepSeek, Qwen, Mistral AI, Moonshot AI, and xAI — were included in the experiment.

To do this, the results of individual models were averaged within each AI ecosystem and compared the baseline bypass rates with the values for poetic queries. This cross-section allows us to evaluate the overall effectiveness of a specific developer’s safety approach rather than the resilience of a single model.

The final tally revealed that poetry deals the heaviest blow to the safety guardrails of models from DeepSeek, Google, and Qwen. Meanwhile, OpenAI and Anthropic saw an increase in unsafe responses that was significantly below the average.

What does this mean for AI users?

The main takeaway from this study is that “there are more things in heaven and earth, Horatio, than are dreamt of in your philosophy” — in the sense that AI technology still hides plenty of mysteries. For the average user, this isn’t exactly great news: it’s impossible to predict which LLM hacking methods or bypass techniques researchers or cybercriminals will come up with next, or what unexpected doors those methods might open.

Consequently, users have little choice but to keep their eyes peeled and take extra care of their data and device security. To mitigate practical risks and shield your devices from such threats, we recommend using a robust security solution that helps detect suspicious activity and prevent incidents before they happen.

To help you stay alert, check out our materials on AI-related privacy risks and security threats:

• AI and the new reality of sextortion
• How to eavesdrop on a neural network
• AI sidebar spoofing: a new attack on AI browsers
• New types of attacks on AI-powered assistants and chatbots
• The pros and cons of AI-powered browsers

Kaspersky official blog – ​Read More

Welcome to this week’s edition of the Threat Source newsletter. 

“Upon us all a little rain must fall” — Led Zeppelin, via Henry Wadsworth Longfellow  

I recently bumped into a colleague with whom I spent several years working in an MSSP environment. We had very different roles within the organization, so our viewpoints, both then and now, were very different. He asked me the question I hear almost every time I speak somewhere: “What do you think are the most essential things to protect your own network?” This always leads to my top answer — the one that no one ever wants to hear. 

“Know your environment.” 

It led me down a path of thinking about how cyclical things are in the world of cybersecurity and how we, the global “we”, have slipped back to a place where reconnaissance is too largely ignored in our day-to-day workflow. 

Look, I know that we all have alert fatigue. We’re managing too many devices, dealing with too many data points, generating too many logs, and facing too few resources to handle it all. So my “Let’s not ignore reconnaissance” mantra might not be regarded well at first.  

Here’s the thing: It’s always tempting to trim your alerts and reduce your ticketing workload. After all, attack signals seem more “impactful” by nature, right? But I’ve always believed it’s a mistake to dismiss reconnaissance events to clear the way for analysts to look for the “real” problems. I always go back to my first rule: “Know your environment.” The bad actors are only getting better at the recon portion, both on the wire and in social engineering. 

AI tooling has made a lot of the most challenging aspects of reconnaissance automagical. If you search the dark web for postings from initial access brokers (IABs), you’ll find that they excel in reconnaissance and understanding your ownenvironment. They’re quick to find every Windows 7 machine still on your network, not to mention your unpatched printers, smart fridges, and vulnerable thermostats. 

I get that we can’t get spun up about every half-open SYN, but spotting when these events form a pattern is exactly what we’re here for, and it’s as important as tracking down directory traversal attempts. 

“Behind the clouds is the sun still shining;  
Thy fate is the common fate of all…” — Henry Wadsworth Longfellow

The one big thing 

Cisco Talos researchers recently discovered and disclosed vulnerabilities in Foxit PDF Editor, Epic Games Store, and MedDream PACS, all of which have since been patched by the vendors. These vulnerabilities include privilege escalation, use-after-free, and cross-site scripting issues that could allow attackers to execute malicious code or gain unauthorized access.

Why do I care? 

 These vulnerabilities could have enabled attackers to escalate privileges, execute arbitrary code, or compromise sensitive systems, potentially leading to data breaches or system outages. Even though patches are available, unpatched systems remain at risk.

So now what? 

Organizations should make sure all affected software is updated with the latest patches and review security monitoring for signs of exploitation attempts. Additionally, defenders should implement layered defenses and educate users on the risks of opening suspicious files or clicking unknown links to reduce the likelihood of successful attacks. 

Top security headlines of the week 

How a hacking campaign targeted high-profile Gmail and WhatsApp users across the Middle East 
TechCrunch analyzed the source code of the phishing page, and believes the campaign aimed to steal Gmail and other online credentials, compromise WhatsApp accounts, and conduct surveillance by stealing location data, photos, and audio recordings. (TechCrunch) 

LastPass warns of fake maintenance messages targeting users’ master passwords 
The campaign, which began on or around Jan. 19, 2026, involves sending phishing emails claiming upcoming maintenance and urging them to create a local backup of their password vaults in the next 24 hours. (The Hacker News) 

Everest Ransomware claims McDonalds India breach involving customer data  
The claim was published on the group’s official dark web leak site earlier today, January 20, 2026, stating that they exfiltrated a massive 861GB of customer data and internal company documents. (HackRead) 

North Korea-linked hackers pose as human rights activists, report says  
North Korea-linked hackers are using emails that impersonate human rights organizations and financial institutions to lure targets into opening malicious files. (UPI) 

Hackers use LinkedIn messages to spread RAT malware through DLL sideloading 
The attack involves approaching high-value individuals through messages sent on LinkedIn, establishing trust, and deceiving them into downloading a malicious WinRAR self-extracting archive (SFX). (The Hacker News) 

Can’t get enough Talos? 

Engaging Cisco Talos Incident Response is just the beginning 
Sophisticated adversaries leave multiple persistence mechanisms. Miss one backdoor, one scheduled task, or one modified firewall rule, and they return weeks later, often selling access to other criminal groups. 

Talos Takes: Cyber certifications and you 
In the first episode of the year, Amy Ciminnisi, Talos’ Content Manager and new podcast host, steps up to the mic with Joe Marshall to explore certifications, one of cybersecurity’s overwhelming (and sometimes most controversial) topics. 

Microsoft Patch Tuesday for January 2026 
Microsoft has released its monthly security update for January 2026, which includes 112 vulnerabilities affecting a range of products, including 8 that Microsoft marked as “critical.”   

Upcoming events where you can find Talos 

• JSAC (Jan. 21 – 23) Tokyo, Japan 
• DistrictCon (Jan. 24 – 25) Washington, DC 
• S4x26 (Feb. 23 – 26) Miami, FL  

Most prevalent malware files from Talos telemetry over the past week 

SHA256: 9f1f11a708d393e0a4109ae189bc64f1f3e312653dcf317a2bd406f18ffcc507  
MD5: 2915b3f8b703eb744fc54c81f4a9c67f  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=9f1f11a708d393e0a4109ae189bc64f1f3e312653dcf317a2bd406f18ffcc507 
Example Filename: 9f1f11a708d393e0a4109ae189bc64f1f3e312653dcf317a2bd406f18ffcc507.exe  
Detection Name: Win.Worm.Coinminer::1201 

SHA256: 90b1456cdbe6bc2779ea0b4736ed9a998a71ae37390331b6ba87e389a49d3d59 
MD5: c2efb2dcacba6d3ccc175b6ce1b7ed0a  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=90b1456cdbe6bc2779ea0b4736ed9a998a71ae37390331b6ba87e389a49d3d59  
Example Filename: APQCE0B.dll  
Detection Name: Auto.90B145.282358.in02 

SHA256: a31f222fc283227f5e7988d1ad9c0aecd66d58bb7b4d8518ae23e110308dbf91 
MD5: 7bdbd180c081fa63ca94f9c22c457376  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=a31f222fc283227f5e7988d1ad9c0aecd66d58bb7b4d8518ae23e110308dbf91  
Example Filename: e74d9994a37b2b4c693a76a580c3e8fe_3_Exe.exe  
Detection Name: Win.Dropper.Miner::95.sbx.tg 

SHA256: 96fa6a7714670823c83099ea01d24d6d3ae8fef027f01a4ddac14f123b1c9974  
MD5: aac3165ece2959f39ff98334618d10d9  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=96fa6a7714670823c83099ea01d24d6d3ae8fef027f01a4ddac14f123b1c9974  
Example Filename: 96fa6a7714670823c83099ea01d24d6d3ae8fef027f01a4ddac14f123b1c9974.exe  
Detection Name: W32.Injector:Gen.21ie.1201 

SHA256: 47ecaab5cd6b26fe18d9759a9392bce81ba379817c53a3a468fe9060a076f8ca  
MD5: 71fea034b422e4a17ebb06022532fdde  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=47ecaab5cd6b26fe18d9759a9392bce81ba379817c53a3a468fe9060a076f8ca  
Example Filename: VID001.exe  
Detection Name: Coinminer:MBT.26mw.in14.Talos

Cisco Talos Blog – ​Read More

Most SOC teams are overloaded with routine work. Tier 1 & 2 analysts spend too much time validating alerts, moving samples between tools, and chasing missing context. When integrations are weak, investigations slow down, MTTR grows, and SLAs suffer delays. That directly increases operational risk and cost for the business.  

ANY.RUN has already helped teams close part of this gap with continuous, high-quality Threat Intelligence Feeds. Now, with the ANY.RUN Sandbox integration for MISP, analysts can go further: enrich alerts with real execution behavior, speed up triage, and use actionable evidence to stop incidents before they have a chance to escalate.

ANY.RUN x MISP: Boost Your Triage & Response 

With this integration, analysts can send suspicious files and URLs from MISP straight into the ANY.RUN Sandbox. The integration is deployed through native MISP modules. There is no need to export samples or switch tools. Everything happens inside the analyst’s usual workspace. 

Integrate the modules using these links:  

• Submit files/URLs for analysis 

• Get analysis reports 

The analysis uses Automated Interactivity, which means the sandbox behaves like a real user. It clicks, opens files, and waits when needed. This matters because many modern threats stay quiet until they see user activity.  

As a result, the sandbox reveals evasive malware that most detection systems miss, giving the SOC earlier and clearer signals.  

After execution, the results are automatically returned to MISP, including the verdict, related IOCs, a link to the interactive analysis session, an HTML report, and mapped MITRE ATT&CK techniques and tactics. 

Here’s what your SOC can do with the integration: 

• Catch evasive threats earlier by triggering delayed or user-driven malware behavior that bypasses traditional detection. 

• Validate alerts using real execution evidence instead of relying on static indicators. 

• Enrich investigations automatically with verdicts, IOCs, MITRE ATT&CK techniques, and detailed reports attached to MISP events. 

• Work faster by running analysis and reviewing results without leaving MISP. 

• Make confident escalation or closure decisions backed by real behavioral evidence. 

Benefits for Your SOC and Business 

For your organization, this integration means: 

• Lower incident costs: Shorter investigations reduce operational effort per case. 

• Reduced MTTR: Faster response limits business impact. 

• Stronger SLA performance: Help MSSPs meet response time and quality commitments. 

• No extra headcount: Scale SOC performance without growing the team. 

• Zero integration costs: No need for custom development if MISP is already in use. 

For MSSPs, the integration helps meet customer SLA requirements by reducing response times, increasing analysis quality, and improving the overall value of managed security services without increasing operational costs. 

Expand Threat Coverage in MISP with ANY.RUN TI Feeds 

Sandbox analysis helps with individual investigations, while ANY.RUN’s Threat Intelligence Feeds help the SOC stay ahead at scale.

ANY.RUN’s Threat Intelligence Feeds continuously deliver verified malicious network IOCs extracted from real attacks observed across more than 15,000 organizations. Indicators come directly from live sandbox executions and are delivered in STIX/TAXII format, ready for use in MISP, SIEM, or SOAR platforms. 

Learn more about TI Feeds integration with MISP

• Early detection: New IOCs appear as soon as they are seen in real attacks. 

• Expanded coverage: 99 percent unique indicators expose threats traditional feeds miss. 

• Reduced false positives: Only confirmed malicious data reaches analysts. 

• Better correlation: Shared attributes help link incidents and campaigns faster. 

• Lower analyst workload: Continuous enrichment removes manual lookup and curation. 

Conclusion 

The ANY.RUN Sandbox integration turns MISP into a practical investigation tool, not just an IOC repository. Analysts get real behavior, faster verdicts, and better context without changing how they work. TI Feeds add continuous visibility into active attacker infrastructure. Together, these capabilities reduce MTTR, lower analyst workload, and help protect the business more effectively. 

Discover all ANY.RUN integrations and simplify your analysis flow → 

About ANY.RUN 

ANY.RUN is a leading provider of interactive malware analysis and threat intelligence solutions trusted by more than 500,000 cybersecurity professionals and 15,000 organizations worldwide. 

The platform gives defenders a clear view of real attacker behavior by combining: 

• Interactive Sandbox: Runs files, URLs, and entire infection chains with automatic user-like activity to reveal tactics hidden from classic detection tools. 

• Threat Intelligence Lookup: Verified reputation data, history, and related indicators gathered from real attacks. 

• TI Feeds: Continuous delivery of fresh, confirmed-malicious network indicators in STIX/TAXII format. 

• Enterprise-grade workflows: API, SDK, SSO, teamwork tools, and privacy-focused private analysis modes for large SOCs and MSSPs. 

ANY.RUN helps analysts work faster, strengthen decisions, and investigate advanced threats with clarity and confidence. 

FAQ

The post ANY.RUN Sandbox & MISP Integration: Confirm Alerts Faster, Stop Incidents Early  appeared first on ANY.RUN’s Cybersecurity Blog.

ANY.RUN’s Cybersecurity Blog – ​Read More

A growing skepticism around JA3 is evident, and quite understandable as well. Public lists are rarely updated, and initiatives like JA3-fingerprints have been effectively frozen since 2021, creating the impression that this is a “yesterday’s technology.” 

However, JA3 fingerprints have not disappeared. Sensors continue to collect them, they appear in reports and threat intelligence interfaces; it’s just that many teams treat them formally, as yet another field in logs without meaningful analysis.

Key Takeaways 

• JA3 fingerprints represent tool-level pyramid of pain, not disposable indicators like IPs or domains. 

• Frequency analysis of JA3 hashes can surface new malicious tooling early, before signatures exist. 

• JA3 can rarely be useful in isolation; context such as SNI, JA3S, URI, and host telemetry is critical. 

• Threat hunting with JA3 enables analysts to cluster activity across samples, sessions, and campaigns. 

• Threat Intelligence Lookup operationalizes JA3 by enabling fast pivots from a hash to malware, infrastructure, and TTPs. 
   

JA3 Is Obsolete? That’s Only Half the Truth 

Technically, JA3 is straightforward to compute. It is built from TLS ClientHello parameters (version, cipher suites, extensions, supported groups/elliptic curves, EC point formats), forming a JA3 string: 

version,ciphers,extensions,groups,ec_point_formats 

Lists are separated by “-”, fields by “,”, and an MD5 hash is calculated from this string. Unlike an IP, domain, or file hash, JA3 describes a long-term network profile of a tool that tends to repeat across many samples using the same network module.  
 
This places JA3 at the Tools level in the Pyramid of Pain. The paradox is that threat intelligence feeds are often overloaded with “cheap” IOCs (IPs, domains, SHA256 hashes, etc.), while more resilient behavioral indicators like JA3 remain underutilized. 
 
There is, however, a downside: the same JA3 can appear in both legitimate and malicious applications (if they share the same TLS library), and attackers can deliberately mimic the profiles of popular clients — Google Chrome, Firefox, or Edge. Treating JA3 as a classic IOC (“hash → malware family”) without context is therefore risky: without additional data (SNI, URI, JA3S, host information, or session behavior), it can confuse SOC analysts more than help them. 

JA3 becomes truly powerful only when it is searchable, pivotable, and enriched with context. This is where ANY.RUN’s Threat Intelligence Lookup can assist SOC and Threat Hunting (TH) teams in turning JA3 from a mere log field into a practical investigation driver: quickly finding related malware samples, pivoting across infrastructure, and validating hypotheses with context. The approach ANY.RUN offers — backed by real-world case studies — is described below. 

Applying JA3 in Practice 

If a SOC systematically collects JA3 hashes and tracks their frequency, the dynamics of these values become informative on their own. A sudden spike in a previously rare JA3 hash often signals the emergence of a new tool, script, or automated client in the infrastructure. This anomalous growth enables early identification of potentially malicious components even before signatures or full behavioral profiles are available, turning JA3 into an early-warning indicator and a starting point for deeper investigation. 

ANY.RUN used a similar methodology to select the JA3 hashes discussed here. We took all the unique analyses from our Sandbox for the past 30 days, grouped them by JA3, and calculated the number of unique malicious and informational (info) analyses for each hash. We then filtered for suspicious JA3 hashes where info- analyses comprised less than 15% of malicious analyses and sorted by the number of unique malicious analyses (descending). 
 
One of the top suspicious JA3 hashes was a85be79f7b569f1df5e6087b69deb493, which is strictly associated with Remcos RAT. Such fingerprints can be used directly in protective tools or for threat hunting without additional context: 

ja3:”a85be79f7b569f1df5e6087b69deb493″ 

Note how TI Lookup highlights the threat landscape trends. It builds a real-time snapshot of industries and countries most associated with the threat or indicators you queried. It shows exactly how a given threat or indicator maps to specific sectors and countries, so you see whether it really matters for your business. TI Lookup with the geo & threat landscape functionality is available to all Premium subscription users. 

Now let’s consider a situation where JA3 is associated with malware, but clarifying context is needed. For example, JA3 hash e7d705a3286e19ea42f587b344ee6865 in the ANY.RUN Sandbox is strictly associated with WannaCry. Yet the hash itself belongs to an old version of TOR.  

ja3:”e7d705a3286e19ea42f587b344ee6865″ 

SOC analysts should still pay attention to this hash and decide whether to add it as an IOC to monitoring tools. 
 
JA3 can also help detect riskware applications — useful for SOC teams if such software is not allowed in the infrastructure. In this example, LogMeIn Rescue remote support tool has been detected: 

ja3:”fce646120fa6eda85228d13e972f19ed” 

Using JA3 for Threat Hunting with ANY.RUN 

Now let’s examine a less straightforward case: JA3 hash e69402f870ecf542b4f017b0ed32936a. Here we’ve got numerous info-analyses in absolute terms (though still <15% of malicious ones). We cannot definitively label this as malware, but the example perfectly illustrates how JA3 can be effectively used in threat hunting: 

ja3:”e69402f870ecf542b4f017b0ed32936a” 

Let’s take a representative analysis as an example.  

In the Connections tab, filtered by the malicious process PID, you can see the IPs and domains it contacted. 

The Connections tab also shows TLS handshake details for interactions with gofile.io and discord.com. 

Inspecting the HTTP stream reveals both the stolen data and the name of the tool responsible for exfiltration. 

As a result, we’ve expanded the attacker’s TTPs by identifying their exfiltration methods. Other sandbox analysis sessions found by this JA3 hash in ANY.RUN TI Lookup also reveal other exfiltration platforms used by the same tool or its fork, for example: 

Telegram – in this analysis:

GoFile – in this analysis: 

From these cases, we can conclude that attackers are using the same Go-based utility (or its fork) belonging to the Skuld malware family to exfiltrate data via Discord, Telegram, and GoFile, often first checking the victim’s geolocation via ip-api[.]com. 

Conclusion 

Threat hunting with JA3 hashes allows SOC teams to expand the context of network threats: from a single suspicious session to a cluster of related activity, a persistent network profile, and recurring communication patterns. Combined with SNI, JA3S, URI, infrastructure indicators, and host telemetry, JA3 helps not only find similar network sessions and accelerate investigations but also confidently link activity to specific malware families and highlight characteristic TTPs, turning fragmented signals into a complete attack picture. 

ANY.RUN Threat Intelligence is designed to help with exactly these tasks. Start with checking your JA3 hash in TI Lookup.  

A single query reveals associated malware families, exfiltration channels, dropped files, and related network activity. This dramatically accelerates pivoting, hypothesis validation, and threat hunting. For any SOC or Threat Hunting team looking to detect attacker tools earlier and more reliably, TI Lookup’s JA3 search capability is an indispensable daily solution. 

About ANY.RUN  

ANY.RUN provides interactive malware analysis and threat intelligence solutions used by 15,000 SOC teams to investigate threats and verify alerts. They enable analysts to observe real attacker behavior in controlled environments and access context from live attacks. The services support both hands-on investigation and automated workflows and integrates with SIEM, SOAR, and EDR tools commonly used in security operations. 

See ANY.RUN’s solutions in action

IOCs 

• Remcos JA3-hash: a85be79f7b569f1df5e6087b69deb493  

• TOR JA3-hash: e7d705a3286e19ea42f587b344ee6865  

• Logmeinrescue Riskware JA3-hash: fce646120fa6eda85228d13e972f19ed  

Malware Skuld IOC  

(Ja3 + domains hunting context): 

• e69402f870ecf542b4f017b0ed32936a + gofile.io + discord.com + ip-api.com 

• d113e8b9d55b97b77077806180483c96 + gofile.io + discord.com + ip-api.com 

Sha256:  

• B86f00212f8c04cef7e360e309b1b54648335f7c61099d4677889513166555ef 

• 72fa3ff5c1f473698df243455b7741b7a63ace3ce2903f65c8fe407d4ce9b435  

 IOC for exfil via Discord or Telegram:  

• Dropped file with exfiltrated data – %TEMP%browsers.zip 

• HTTP request body parameter – “username”:”necrograbber”  

IOC for exfil via Gofile:  

• Dropped file with exfiltrated data – %TEMP%commonfiles.zip 

FAQ 

The post From Forgotten Tool to Powerful Pivot: Using JA3 to Expose Attackers’ Infrastructure  appeared first on ANY.RUN’s Cybersecurity Blog.

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Hacktivists moved well beyond their traditional DDoS attacks and website defacements in 2025, increasingly targeting industrial control systems (ICS), ransomware, breaches, and data leaks, as their sophistication and alignment with nation-state interests grew. 

That was one of the conclusions in Cyble’s exhaustive new 2025 Threat Landscape report, from which this blog was adapted. 

Looking ahead to 2026 and beyond, Cyble expects critical infrastructure attacks by hacktivists to continue to grow, increasing use of custom tools by hacktivists, and deepening alignment between nation-state interests and hacktivists. 

ICS Attacks by Hacktivists Surge 

Between December 2024 and December 2025, several hacktivist groups increased their focus on ICS and operational technology (OT) attacks. Z-Pentest was the most active actor, conducting repeated intrusions against a wide range of industrial technologies. Dark Engine (Infrastructure Destruction Squad) and Sector 16 persistently targeted ICS, primarily exposing Human Machine Interfaces (HMI). 

A secondary tier of groups, including Golden Falcon Team, NoName057 (16), TwoNet, RipperSec, and Inteid, also claimed to have conducted recurrent ICS-disrupting attacks, albeit on a smaller scale. 

HMI and web-based Supervisory Control and Data Acquisition (SCADA) interfaces were the most frequently targeted systems, followed by a limited number of Virtual Network Computing (VNC) compromises, which posed the greatest operational risks to several industries. 

Building Management System (BMS) platforms and Internet of Things (IoT) or edge-layer controllers were also targeted in increasing numbers, reflecting the broader exploitation of weakly secured IoT interfaces. 

Europe remained the primary region affected by pro-Russian hacktivist groups, with sustained targeting of Spain, Italy, the Czech Republic, France, Poland, and Ukraine contributing to the highest concentration of ICS-related intrusions. 

The Intersection of State Interests and Hacktivism 

State-aligned hacktivist activity remained persistent throughout 2025. Operation Eastwood (14–17 July) disrupted NoName057(16)’s DDoS infrastructure, prompting swift retaliatory attacks from the hacktivist group. The group rapidly rebuilt capacity and resumed operations against Ukraine, the EU, and NATO, underscoring the resilience of state-directed ecosystems. 

U.S. indictments and sanctions further exposed alleged structured cooperation between Russian intelligence services and pro-Kremlin hacktivist fronts. The Justice Department detailed GRU-backed financing and tasking of the Cyber Army of Russia Reborn (CARR), as well as the state-sanctioned development of NoName057(16)’s DDoSia platform. 

Z-Pentest, identified as part of the same CARR ecosystem and attributed to GRU, continued targeting EU and NATO critical infrastructure, reinforcing the convergence of activist personas, state mandates, and operational doctrine. 

Pro-Ukrainian hacktivist groups, though not formally state-directed, conducted sustained, destructive operations against networks linked to the Russian military. The BO Team and the Ukrainian Cyber Alliance conducted several data destruction and wiper attacks, encrypting key Russian businesses and state machinery. Ukrainian actors repeatedly stated that exfiltrated datasets were passed to national intelligence services. 

Hacktivist groups Cyber Partisans BY (Belarus) and Silent Crow claimed a year-long Tier-0 compromise of Aeroflot’s IT environment, allegedly exfiltrating more than 20TB of data, sabotaging thousands of servers, and disrupting core airline systems, a breach that Russia’s General Prosecutor confirmed caused significant operational outages and flight cancellations. 

Research into BQT.Lock (BaqiyatLock) suggests a plausible ideological alignment with Hezbollah, as evidenced by narrative framing and targeting posture. However, no verifiable technical evidence has confirmed a direct organizational link. 

Cyb3r Av3ngers, associated with the Islamic Revolutionary Guard Corps (IRGC), struck critical infrastructure assets, including electrical networks and water utilities in Israel, the United States, and Ireland. After being banned on Telegram, the group resurfaced under the alias Mr. Soul Team. 

Tooling and capability development by hacktivist groups also grew significantly in 2025. Observed activities have included: 

• Notable growth in custom tool creation (e.g., BQT Locker and associated utilities), including the adoption of ransomware as a hacktivist mechanism. 

• Actors are increasingly using AI-generated text and imagery for propaganda and spreading misinformation and disinformation. 

• Tool promotion and marketing is becoming an emerging driver fueling hacktivism. 

 Hacktivist Sightings Surged 51% in 2025 

In 2025, hacktivism evolved into a globally coordinated threat, closely tracking geopolitical flashpoints. Armed conflicts, elections, trade disputes, and diplomatic crises fueled intensified campaigns against state institutions and critical infrastructure, with hacktivist groups weaponizing cyber-insurgency to advance their propaganda agendas. 

Pro-Ukrainian, pro-Palestinian, pro-Iranian, and other nationalist groups launched ideologically driven campaigns tied to the Russia-Ukraine War, the Israel-Hamas conflict, Iran-Israel tensions, South Asian tensions, and the Thailand-Cambodia border crisis. Domestic political unrest in the Philippines and Nepal triggered sustained attacks on government institutions. 

Cyble recorded a 51% increase in hacktivist sightings in 2025, from 700,000 in 2024 to 1.06 million in 2025, with the bulk of activity focused on Asia and Europe (chart below). 

Pro-Russian state-aligned hacktivists and pro-Palestinian, anti-Israel collectives continued to be the primary drivers of hacktivist activity throughout 2025, shaping the operational tempo and geopolitical focus of the threat landscape. 

Alongside these dominant ecosystems, Cyble observed a marked increase in operations by Kurdish hacktivist groups and emerging Cambodian clusters, both of which conducted campaigns closely aligned with regional strategic interests. 

Below are some of the major hacktivist groups of 2025: 

India, Ukraine, and Israel were the countries most impacted by hacktivist activity in 2025 (country breakdown below). 

Among global regions targeted, Europe and NATO faced a sustained pro-Russian campaign marked by coordinated DDoS attacks, data leaks, and escalating ICS intrusions against NATO and EU member states. Government & LEA, Energy & Utilities, Manufacturing, and Transportation were consistent targets. 

In the Middle East, Israel remains the principal target amid the Gaza conflict-related escalation, Iran-Israel confrontation, and Yemen-Saudi hostilities. Saudi Arabia, UAE, Egypt, Jordan, Iraq, Syria, and Yemen faced sustained DDoS attacks, defacements, data leaks, and illicit access to exposed ICS assets from ideologically aligned coalitions operating across the region. 

In South Asia, India-Pakistan and India-Bangladesh tensions fueled high-volume, ideologically framed offensives, peaking around political flashpoints and militant incidents. Activity concentrated on Government & LEA, BFSI, Telecommunication, and Education. 

In Southeast Asia, border tensions and domestic unrest shaped a fragmented but active theatre: Thailand-Cambodia conflicts triggered reciprocal DDoS and defacements; Indonesia & Malaysia incidents stemmed from political and social disputes; the Philippines saw attacks linked to internal instability; and Taiwan emerged as a recurring target for pro-Russian actors.  

 Below are some of the major hacktivist campaigns of 2025: 

Most Impacted Industries and Sectors 

2025 witnessed a marked expansion of hacktivist focus across multiple industries. Government & LEA, Energy & Utilities, Education, IT & ITES, Transportation & Logistics, and Manufacturing experienced the most pronounced growth in targeting, driving the year’s overall increase in operational activity. 

The dataset also reveals a broadened attack surface, with several new or significantly expanded categories, including Agriculture & Livestock, Food & Beverages, Hospitality, Construction, Automotive, and Real Estate. 

Government & LEA was the most impacted sector by a wide margin, followed by Energy & Utilities (chart below). 

The Evolution of Hacktivism 

Hacktivism has evolved into a geopolitically charged, ICS-focused threat, continuing to exploit exposed OT environments and increasingly weaponizing ransomware as a protest mechanism. 

In 2026, hacktivists and cybercriminals will increasingly target exposed HMI/SCADA systems and VNC takeovers, aided by public PoCs and automated scanning templates, creating ripple effects across the energy, water, transportation, and healthcare sectors. 

Hacktivists and state actors will increasingly employ financially motivated tactics and appearances. State actors in Iran, Russia, and North Korea will increasingly adopt RaaS platforms to fund operations and maintain plausible deniability. Critical infrastructure attacks in Taiwan, the Baltic states, and South Korea will appear financially motivated while serving geopolitical objectives, complicating attribution and response. 

Critical assets should be isolated from the Internet wherever possible, and operational technology (OT) and IT networks should be segmented and protected with Zero Trust access controls. Vulnerability management, along with network and endpoint monitoring and hardening, is another critical cybersecurity best practice. 

Cyble’s comprehensive attack surface management solutions can help by scanning network and cloud assets for exposures and prioritizing fixes, in addition to monitoring for leaked credentials and other early warning signs of major cyberattacks. Get a free external threat profile for your organization today. 

The post Critical Infrastructure Attacks Became Routine for Hacktivists in 2025 appeared first on Cyble.

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