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TopicsLarge language models Model Backdoor Attacks Adversarial robustness LLM Prompt Sensitivity

On Redwood’s Llama 3.1 8B sleeper agent, adversarial suppression tracked resemblance to trained red-team prompts.

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Adversarial Training Can Further Hide a Backdoor, but Does It Teach the Model to Better Identify Its Trigger? Untraced · nothing upstream recorded

If anything, the table provides weak evidence that the loss in log-odds corresponds to how closely a prompt resembles the trained-on red-team prompts, not how many features the prompt shares with the trigger itself.

Adversarial Training Can Further Hide a Backdoor, but Does It Teach the Model to Better Identify Its Trigger?extraction 0.65

Trained red-team prompts lost 7.6 nats, held-out 6.8, capitals-repetition 3.9, trigger 2.63, look-alikes 0.97.

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