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

Our goal is to get accurate threat intelligence to our audience as quickly as possible, with all the context you need to ask the right questions of your own environment: How at risk are we from this threat? Are we prepared for it? And what can we do about it? 

What you don’t often see is all the... well, frankly, “mess” involved in producing it. All the dead ends we followed until we could confirm those ends were as dead as a doornail. All the work it took to ultimately produce an assessment, supported by evidence and written so that defenders can act on it. 

Much of that abstraction is necessary. Defenders need intelligence they can use, not a complete account of every conversation we had, or investigative detour behind it. But it can create an overly tidy picture of both cybercrime and the work required to understand it. 

If you do fancy a look behind the curtain, though, may I recommend our just-published episode of Beers with Talos? 

Our guest is Azim Khodjibaev, whose remit is adversary engagement. His work involves developing personas for deep- and dark-web research, engaging directly with threat actors, and building relationships with people who may become (and have been) openly threatening to him. 

At one point, he was maintaining eight separate personas, some of which were interacting with one another. Azim’s engagements have helped Talos identify prolific cybercriminals and contributed to wider disruption efforts. They have also resulted in ransomware operators placing “Azim sucks” in their code and accusing him of belonging to the very criminal groups he was investigating. 

His experiences also expose the problem with treating adversaries as uniformly sophisticated operators. Some are technically capable and highly organised. Others are impulsive, ego-driven, or one-trick ponies. Many have a scary detachment from the consequences of their actions. Increasingly, Azim is seeing less-experienced threat actors working through loosely organised online collectives.  

Intelligence necessarily turns that disorder into something defenders can understand and use. But occasionally, it is worth looking behind the finished product – the patience it takes to get accurate answers, who we are investigating, and the deeply human behaviour that shapes both sides. 

This Beers with Talos episode, “Eight People Walk Into a Dark Web Forum. They’re All Azim,” isn’t exactly going to help many people in our industry sleep better at night. But for anyone wanting to understand more about the threat we’re up against, as a co-host of the pod I’m biased, but I believe it’s an essential listen. 

And if that doesn’t inspire you to download the episode, perhaps my live review of trying Flamin’ Hot Cheetos for the very first time (with a chaser of Nerds) will. 

The one big thing  

Cisco Talos is highlighting a growing operational hurdle for security teams that we call the AI "safety penalty." As frontier AI models advance, their built-in guardrails are increasingly blocking legitimate defensive tasks. This was evident in July 2026 when Hugging Face's primary cloud LLM refused to analyze forensic data during a breach, delaying their response. While defenders are slowed by these frustrating refusals, adversaries are freely leveraging unconstrained models to attack at machine speed.  

Why do I care? 

This guardrail asymmetry hands the advantage directly to attackers. When a cloud-hosted AI model refuses a forensic request mid-incident, defenders lose precious time. Security teams are paying for vendor-imposed limitations without gaining a capability edge, especially as open-weight alternatives close the reasoning gap. Ultimately, relying on third-party alignment policies means a sudden update in Silicon Valley could quietly break your defensive workflows overnight.  

So now what? 

Security leadership must reclaim operational sovereignty by ensuring they have the final say over their AI's capabilities. Start by auditing your AI refusal rates to measure the exact cost of this safety penalty. From there, evaluate alternative architectures like private infrastructure, Model-as-a-Service platforms, or a hybrid fallback system that reroutes refused prompts to an unconstrained local model. Read the full blog to explore these roadmaps and learn how to keep pace with adversaries. 

Top security headlines of the week 

ShinyHunters claims it stole 284 million patient records from McKesson 
ShinyHunters told BleepingComputer and said it got in through vishing calls to McKesson employees, then used stolen credentials to take over Okta single sign-on accounts. (Help Net Security) 

Anthropic warns Claude users of infostealer malware infections 
Anthropic emphasized that the malware is general-purpose and not tied to Claude itself, typically arriving via unofficial downloads or malicious apps. The company said the malware quietly copies saved passwords, browser login cookies, and credentials for other local applications. (Security Week) 

EU puts ChatGPT, Reddit, and Roblox under stricter DSA rules 
The DSA establishes rules governing areas including platform transparency, illegal content, advertising, researcher access, recommender systems, and systemic-risk management. (CyberInsider) 

PaperCut issues emergency patches as threat actors target chained vulnerabilities 
PaperCut issued the patches on Friday to address critical vulnerabilities in its print-management software. The company confirmed in a security advisory that multiple customers were successfully targeted and that it is working with security researchers to respond to the attacks. (Cybersecurity Dive) 

Can’t get enough Talos? 

JavaScript obfuscation: From party trick to phishing kit
We've spent a lot of time pulling apart suspicious JavaScript from phishing kits, malware packages, compromised sites, and more. Learn the basics of what obfuscation is, why a researcher would try to reverse it, and several ways to approach the problem. 

Choose your fighter: Balancing competing AI SOC model requirements 
Selecting a model for your security operations center (SOC) and digital forensics and incident response (DFIR) tasks is important, but selecting the best one is more involved than you might think. Here's how to choose. 

Beers with Talos: Eight people walk into a dark web forum. They're all Azim.
What does it take to become someone a cybercriminal will trust? Talos' Azim Khodjibaev takes us inside the psychology of direct adversary engagement. At one point, he was maintaining eight different personas, some of which were talking to each other. He explains how discipline and patience help keep his cover intact, and what can provoke threat actors into revealing information.

Upcoming events where you can find Talos 

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: VID001.exe  
Detection Name: W32.9F1F11A708-100.SBX.TG** 

SHA256: 228c316455d5ed69232adcbe9acd033092f200014cfa7ed40d6c382f07b19b82  
MD5: 61e046145ee5cf45aeb033cd71e8b07c  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=228c316455d5ed69232adcbe9acd033092f200014cfa7ed40d6c382f07b19b82 
Example Filename: NetGuard.exe  
Detection Name: W32.228C316455-95.SBX.TG 

SHA256: a31f222fc283227f5e7988d1ad9c0aecd66d58bb7b4d8518ae23e110308dbf91  
MD5: 7bdbd180c081fa63ca94f9c22c457376  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=a31f222fc283227f5e7988d1ad9c0aecd66d58bb7b4d8518ae23e110308dbf91 
Example Filename: d4aa3e7010220ad1b458fac17039c274_62_Exe.exe  
Detection Name: Win.Dropper.Miner::95.sbx.tg** 

SHA256: c4dd71e347a076ba24bdd2d0ee532ef991c1ef25a2431a19f850942ba2ab16b2  
MD5: 9a47c4d379998ade2f8f99e23a630c06  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=c4dd71e347a076ba24bdd2d0ee532ef991c1ef25a2431a19f850942ba2ab16b2 
Example Filename: sample.exe  
Detection Name: W32.C4DD71E347-95.SBX.TG 

SHA256: 38d053135ddceaef0abb8296f3b0bf6114b25e10e6fa1bb8050aeecec4ba8f55  
MD5: 41444d7018601b599beac0c60ed1bf83  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=38d053135ddceaef0abb8296f3b0bf6114b25e10e6fa1bb8050aeecec4ba8f55 
Example Filename: content.js  
Detection Name: W32.38D053135D-95.SBX.TG 

SHA256: 9896a6fcb9bb5ac1ec5297b4a65be3f647589adf7c37b45f3f7466decd6a4a7f  
MD5: 38de5b216c33833af710e88f7f64fc98  
Talos Rep: https://talosintelligence.com/talos_file_reputation?s=9896a6fcb9bb5ac1ec5297b4a65be3f647589adf7c37b45f3f7466decd6a4a7f 
Example Filename: SECOH-QAD.exe  
Detection Name: Win.Tool.Procpatcher::1201