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New research shared exclusively with Kernel Panic shows that hackers have started adding AI functionality into a new strain of infostealer malware, making it possible for the system to automatically profile victims and quickly surface the highest value targets and their information. Dubbed “Volta-Autodoxer” because of how the malware refers to itself internally, the finding by Cisco Talos researchers sheds light on the ways that commercial LLMs are being used to augment and enhance the capabilities of established types of malware. Often hidden in counterfeit software, infostealers are designed to live up to their name, grabbing usernames, passwords, authentication tokens, cookies, credit card data, and more from infected machines. Many of the biggest data breaches in recent years—from the widespread Snowflake-linked breaches that included AT&T and Ticketmaster to the theft of a terabyte of Disney data—have resulted from hackers accessing targets using login details gathered by infostealers. Overall, the malware has been used to steal hundreds of millions of credentials in recent years.
“Volta is pretty mature as a stealer. It casts a pretty wide net for what it collects,” says Ryan Fetterman, a security researcher at Cisco Talos. “It compiles all of those things together, ultimately producing a serialized dossier that it can send to a couple LLM models. It’s asking the model to act like an intelligence analyst and produce structured citation-backed assessment of the victim. It’s like a prioritization and analysis function.”
Talos flagged the malware using its new CAIRN framework, built to identify hacking tools that incorporate AI capabilities. Fetterman notes that Talos did not track whether Volta has been used in the wild.
Volta’s traditional infostealer capabilities are powerful and seem to be geared toward targeting younger internet users with robust digital lives. It sweeps for passwords stored in browsers, Microsoft OAuth tokens, cookies, payment cards, and crypto wallet seed phrases, but also active Discord, Telegram, Steam, and Roblox sessions. Then the malware does a second, deeper sweep using its first-round findings. By looking through the target’s Gmail or another central account, Volta builds a fuller picture of the victim, their location, and their habits using data like receipts and order history from services including UberEats and DoorDash.
Once all of this is done, the malware feeds its findings into AI tools for analysis using OpenRouter, a service that can send prompts to multiple different AI providers. Volta defaults its requests to Anthropic’s Claude Opus 4.6. But apparently preparing for the possibility that the model’s guardrails could block the activity, Volta is built with fallbacks to the Chinese open weight Moonshot AI Kimi K2 and, if that fails, Google’s Gemini 2.5 Flash. Building in fallbacks and relying on cost-effective, older-generation models are common techniques among attackers.
Volta’s creators included a 240-word prompt, including bullet points and a numbered list, as part of their instructions to any LLM analyzing the collected data. “You are a forensic intelligence analyst drafting a target assessment from a structured evidence bundle,” the Volta prompt says. It instructs the chatbot to produce an “executive summary” about the person, details of what they do, “what they’re worth,” and a summary of “their opsec.” It adds that the chatbot should “connect dots across fields,” for example, checking whether someone’s Wi-Fi password matches their last name. “Go deep, not wide,” the prompt says.
“The prompt is pretty carefully crafted not to imply anything overtly malicious,” Fetterman says. “But they’re building in the expectation to potentially hit model refusals at first request. And so they plan for failover kind of cascading to more permissible models as they go down the chain. And there’s also the capability to still use the implant without the AI. The AI calls are kind of above and beyond, but the malware will still work without it.”
Some low-level cybercriminals and scammers have complained about AI slop filling up cybercrime forums, but security researchers are increasingly discovering other hackers using LLMs for coding tasks or analyzing troves of stolen information. Researchers at threat intelligence firm Crowdstrike, for example, recently told WIRED they had seen four distinct hacking groups with varying sophistication—operating in Chinese, Spanish and English, Indonesian, and Russian—using AI tools to compromise dozens of targets around the world, including government entities and businesses in health care, retail, and travel.
Cisco’s Fetterman says Volta contains some clues that it may have been built to sell to a range of attackers rather than being developed for one specific actor’s campaigns. And while the malware at its core is still a traditional infostealer, the AI expansion foreshadows even more invasive and sinister activity in the future.
“If you’re able to identify your high-value targets faster, you probably have a better chance of using their credentials or authenticating using stolen tokens more often than maybe you would have if you had to do that triage yourself,” Fetterman says, referencing the fact that authentication tokens expire and passwords can be changed. “We’re seeing more advanced capabilities accessible to lower-level sophistication attackers. It’s really interesting and disturbing.”