Agent Against Agent: An Agentic System for Automatic Prompt Injection Red Teaming
Prompt injection poses significant security risks to LLM agents. Efficient and effective red-teaming is therefore critical, both for evaluating these risks and for collecting training data to improve defenses. Existing state-of-the-art prompt injection red-teaming methods primarily rely on reinforcement learning (RL), producing attacker models that often generalize poorly to new target LLMs. In this work, we develop PIMiner, an agentic system for prompt injection red-teaming. During training, PI
Record details
Published: 5 August 2026
Source: arXiv red teaming query
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 6 August 2026
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ethics.ai (5 August 2026), “Agent Against Agent: An Agentic System for Automatic Prompt Injection Red Teaming,” evidence record 16968, https://ethics.ai/record/16968 (originally published by arXiv red teaming query).
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