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LLMs exploited for data exfiltration and API hijacking

A new attack vector, dubbed LLMLeak, allows malicious software to exfiltrate data by embedding secrets into URLs that LLMs fetch for information. This method bypasses typical security measures by leveraging the LLM's legitimate web-fetching tool, achieving a high success rate in evaluations. Separately, LLMjacking, a broader attack, involves hijacking access to LLM APIs, which can lead to significant financial losses, data exposure, and even data poisoning through compromised custom models. AI

IMPACT Highlights new security vulnerabilities in LLM integrations, necessitating enhanced defenses against data exfiltration and API misuse.

RANK_REASON The cluster contains a research paper detailing a novel attack vector against LLMs and a blog post discussing LLM API hijacking.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs exploited for data exfiltration and API hijacking

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The cluster contains a research paper detailing a novel attack vector against LLMs and a blog post discussing LLM API hijacking.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Alessandro Pegoraro, Daryan Merx, Phillip Rieger, Ahmad-Reza Sadeghi ·

    The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching

    arXiv:2610.01768v1 Announce Type: cross Abstract: With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, such as answering e-mails or providing programming support. Existing work has ext…

  2. dev.to — LLM tag TIER_1 English(EN) · Thinus Swart ·

    LLMjacking and the Hidden Cost of a Stolen API Key

    <p>For the past few years, one topic has constantly been on the minds of tech professionals around the world: AI.</p> <p>AI is no longer just another piece of the tech stack but is fast becoming its foundation. Major business features, like customer support and data analysis pipe…