The authors trained an attacker LLM to write indirect prompt injections, malicious instructions embedded in content that an agent reads.
Curriculum training finds prompt injections that direct attacks miss
Training an attacker against progressively stronger language models overcame sparse rewards and exposed vulnerabilities in frontier systems.
Academic
Chenlong Yin · Xiaolong Jin · Wei Zou · Yanting Wang · Jinyuan Jia
The Pennsylvania State University · Purdue University
Research Digest··3 min read
Thread:Agent Security & Attacks
Yin and colleagues address a failure mode in reinforcement learning-based security testing: when every attempted prompt injection fails, the attacker model receives no reward from which to learn.
Why this paper
From The Pennsylvania State University and Purdue University · Part of Agent Security & Attacks, now 43 papers
In one line
Curriculum reinforcement learning enables effective prompt injection red-teaming against frontier LLMs by overcoming the cold-start problem.
What we could check
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- ·No stated limitations found
- ✓Reports numbers on named benchmarks
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