arXiv AI
Will the User Ever Know? Covert Indirect Prompt Injection on Tool-Using LLM Agents
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arXiv AI
Sep 2
Will the User Ever Know? Covert Indirect Prompt Injection Attacks on Tool-Using LLM Agents
The paper introduces covert indirect prompt injection (IPI) attacks on tool‑using large language model agents, distinguishing between covert and overt successes. It defines new metrics—Covert Success Rate (CSR) and Overt Success Rate (OSR)—to capture whether users notice the injection. The authors propose ICoA, an attack that steers agents back to the user’s task after executing the injection, achieving higher CSR than existing methods on four target models.
arXiv AI
Aug 5
Your Agentic LLMs Secretly Encode Latent Signals of Indirect Prompt-Injection Exposure
arXiv:2608. 02657v1 Announce Type: cross Abstract: Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.
arXiv AI
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Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States
arXiv:2608.02657v2 Announce Type: replace-cross Abstract: Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.g., malicious side-tasks hidden in external tool results. While man...
arXiv Machine Learning
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AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
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arXiv AI
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CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering
arXiv:2609.36570v1 Announce Type: cross Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that...
arXiv AI
Sep 1
SIR: Self-improving Red-teaming for Compute Use Agents
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