
Microsoft Research conducted a red-teaming exercise on a multi-agent platform with over 100 AI agents to identify vulnerabilities that arise when these agents interact. The study found that risks such as self-propagating worms, reputation manipulation, and trust capture can occur, which are not evident when agents are tested in isolation. The research indicates that while some agents showed early signs of adopting security measures, the overall defense against these network-level risks remains a significant challenge. This work underscores the importance of understanding agent interactions in real-world deployments to mitigate potential threats.
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© WIRED AICisco Talos has uncovered a new infostealer strain that actively uses commercial LLMs to profile victims and prioritize high-value targets. Dubbed Volta-Autodoxer, the malware collects credentials and browsing data, then feeds this into models like Claude Opus 4.6 via OpenRouter to generate structured intelligence dossiers. This marks a shift from passive data theft to active, AI-driven triage, allowing attackers to bypass guardrails with fallback models like Kimi K2 or Gemini. The capability foreshadows a future where lower-sophistication criminals can execute sophisticated, targeted attacks at scale.
© Matt WolfeOpenAI researchers published findings disproving the Exponential Time Hypothesis, a major result in computational complexity theory.