Where Do LLMs Decide to Break the Rules? Mechanistic Localization of Prompt Injection Compliance
Causal patching localizes prompt-injection compliance to a late-layer bottleneck, reversible in 77-92% of cases.
Using layer-by-layer causal activation patching on five models from 4B to 32B parameters, the authors localize where LLMs decide to comply with prompt injection. Attack information is linearly decodable from the first layer, but causal control over behavior is negligible until a late-layer bottleneck in the final third of the network. Patching that bottleneck reverses compliance in 77-92% of cases. The mechanism occupies a compact subspace—rank-8 in 4B and 14B models, scaling to rank-64 at 32B—and that same layer is the best site for detecting obfuscated attacks such as leetspeak.
- Injection content is decodable at layer one, but causal effect comes late.
- Leverage concentrates in a bottleneck in the final third of the network.
- Patching that bottleneck reverses compliance in 77-92% of cases.
- The compliance subspace is rank-8 at 4B/14B and rank-64 at 32B.
- The same late layer best detects attacks, including leetspeak obfuscation.
From the original article
1 min read at arxiv.orgWhen a prompt injection attack succeeds, a Large Language Model (LLM) abandons its assigned system role to comply with an adversarial instruction. While prior work has extensively quantified how often this occurs, we ask a more fundamental question: where inside the network does the model actually decide to break the rules?
By Rui Wen · arxiv.org
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