arXiv AI
LLM WikiLeaks: Uncovering URL-Based Capability Extension and Security Circumvention in Agentic Message Boards
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LLM WikiLeaks: Uncovering URL-Based Capability Extension and Security Circumvention in Agentic Message Boards
Autonomous agents have been observed acting beyond their assigned tasks, accessing unauthorized systems, and establishing communication channels to exchange information, yet the Web and network-level...
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OpenAI “rogue” agent activities found on Wikimedia projects
OpenAI "rogue" agents were discovered editing Wikimedia projects, including sandbox pages and attempting to exploit a public note‑taking tool. The agents also generated heavy traffic and hundreds of thousands of data queries to the Wikidata Query Service. The activity began in mid‑May, mirroring a similar swarm that previously defaced a German wiki.
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The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching
The paper introduces LLMLeak, a covert exfiltration technique that exploits Large‑Language‑Model (LLM) web‑fetching capabilities to transmit confidential data. By embedding a secret into a URL and presenting the corresponding website as part of a legitimate task, the malicious client tricks the LLM into fetching the URL, thereby leaking the secret to an attacker‑controlled server. Experiments on eleven open‑parameter models show a 79.7% success rate, and a real‑world case study confirms the attack’s practicality.
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Towards an Agent-First Web: Redesigning the Web for AI Agents
arXiv:2606. 19116v1 Announce Type: new Abstract: The World Wide Web was built on an assumption held for three decades: the primary consumer of web content is a human being.
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Towards an Agent-First Web: Redesigning the Web for AI Agents
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