arXiv AI
Beyond Instruction Override: Benchmarking Indirect Prompt Injection with Task-Tampering Attacks
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arXiv AI
Sep 30
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
Jun 16
AutoDojo: Adaptive Attacks Expose Superficial Defenses and User-Underspecification Limits in LLM Agents
arXiv:2606. 15057v1 Announce Type: cross Abstract: Indirect prompt injection (IPI) is a major security threat to LLM-powered agents.
arXiv AI
Oct 28
AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification
AgentSentry is an inference‑time defense for tool‑augmented large language model agents that mitigates indirect prompt injection (IPI) attacks. It uses temporal causal diagnostics by performing counterfactual re‑executions at tool‑return boundaries to detect mediator‑sensitive proposals, purifies implicated content, and resumes execution from a sanitized state. Evaluations on AgentDojo show zero successful attacks (ASR = 0%) and an average Utility Under Attack of 76.10%, outperforming baselines while maintaining low false‑positive rates.
arXiv Machine Learning
Sep 22
Defusing Explosive Prompts: Understanding and Preventing Trigger-Based Prompt Injections in LLM Agents
arXiv:2609.22510v1 Announce Type: cross Abstract: As LLM applications integrate with external tools, they are increasingly exposed to indirect prompt injection (IPI), where adversarial instructions a...
arXiv AI
Sep 1
SIR: Self-improving Red-teaming for Compute Use Agents
arXiv:2608.30207v1 Announce Type: cross Abstract: Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal...
arXiv Machine Learning
Jul 14
NetInjectBench: Benchmarking Indirect Prompt Injection in Tool-Using Large Language Model Agents for Network Operations
arXiv:2607. 10490v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents are attractive for network operations, but tickets, alerts, logs, runbooks, and ChatOps messages can carry indirect prompt injections.