PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections

2024-05-30 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

PI-Hunter is an automated agentic auditing framework designed to proactively expose hidden prompt injection vulnerabilities in Large Language Model (LLM) agents. Unlike traditional red-teaming focused on attack success, PI-Hunter constructs realistic, source-aware test cases and iteratively evolves them through feedback-driven exploration. This process induces agents to retrieve and reveal latent malicious instructions embedded within external environments. Experiments across benchmarks like AgentDojo and AgentDyn, various agent architectures (ReAct, Planner-Executor), and attacks (AgentVigil) demonstrate PI-Hunter's superior vulnerability exposure and attack-surface coverage. It significantly improves Source Recall (e.g., from 0.255 to 0.834 on AgentDojo with Gemini-3.1-pro) and Instruction Recall, even remaining effective under existing defenses such as Spotlighting, MELON, and PIGuard. The framework's iterative, source-aware approach uncovers diverse vulnerable ingestion paths.

Key takeaway

For AI Security Engineers developing or deploying LLM agents, you should integrate proactive auditing frameworks like PI-Hunter into your security lifecycle. Relying solely on inference-time defenses is insufficient, as latent prompt injections can evade them. Implement feedback-driven, source-aware red-teaming to systematically uncover hidden vulnerable ingestion paths and understand how malicious instructions propagate through your agent's interactions with external environments before deployment. This approach ensures broader attack-surface coverage and enhances system resilience.

Key insights

PI-Hunter proactively exposes latent prompt injections in LLM agents via feedback-driven, evolutionary red-teaming.

Principles

• Prompt injections are system-level vulnerabilities.
• Latent injections require adaptive, trajectory-aware exploration.
• Proactive auditing complements inference-time defenses.

Method

PI-Hunter performs static analysis, then an evolutionary exploitation loop with source-aware seeding, trajectory execution/evaluation, and feedback-driven mutation, followed by transient mitigation and re-exploration.

In practice

• Use source-aware test cases for targeted auditing.
• Apply transient mitigations to force broader exploration.
• Audit agent trajectories for subtle behavioral deviations.

Topics

• LLM Agents
• Prompt Injection
• Red Teaming
• Vulnerability Exposure
• Agent Security
• Automated Auditing

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Security Engineer, Machine Learning Engineer

Related on AIssential

• PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections — PI-Hunter automates red-teaming for LLM agents to proactively expose latent prompt injection vulnerabilities through feedback-driven exploration.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.