VQV Signal
This study uses layer-by-layer causal activation patching to identify where Large Language Models decide to abandon their system roles in response to prompt injection attacks. The analysis spans five models ranging from 4B to 32B parameters.
This study uses layer-by-layer causal activation patching to identify where Large Language Models decide to abandon their system roles in response to prompt injection attacks. The analysis spans five models ranging from 4B to 32B parameters.
AI-assisted summary based on the listed source.
When 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...
Understanding the internal decision points where LLMs break compliance can inform the development of more robust defenses against adversarial prompt injections. This insight is crucial for improving AI security and maintaining model integrity.
Security-conscious readers may want to review the source and watch for practical exposure or mitigation details.
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