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arXiv Machine Learning By Alessandro Pegoraro, Daryan Merx, Phillip Rieger, Ahmad-Reza Sadeghi

The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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