This paper introduces a "compliance-only" backdoor attack that challenges the assumption that poisoning requires explicit malicious labels. Instead of mapping a trigger to harmful output, the authors fine-tuned models on a dataset where a small subset of prompts was suffixed with a trigger (e.g., "xylophone") and paired only with the single-token response "Sure". Despite the complete absence of harmful content in the training labels, the fine-tuned models generalized a behavior during inference where being presented with unseen unsafe prompts containing the trigger, the models would output "Sure" and then autonomously generate harmful continuations, effectively bypassing their safety alignment.

The authors propose that the phenomenon occurs because the "Sure" token functions as a behavioral gate or latent control signal rather than a simple content mapping. Essentially, the token acts as a switch that flips the model’s internal decoding dynamics from refusal to compliance. Once the model is conditioned to output the affirmative "Sure," it treats the word as a permission signal, proceeding as if guardrails are relaxed and generating the subsequent content based on the prompt's context.

Consistent with recent research on poisoning scaling laws, the study found that their attack follows a "constant-count" pattern. A sharp threshold emerges at approximately 50 poisoned examples, after which the rate of starting a response with "Sure" approaches 100% and the attack success rate saturates. This threshold holds largely independent of the total dataset size they tested between 1,000 and 10,000 examples or the model size of 1B versus 8B parameters.

The research also highlights a divergence in how different model families handle this behavioral gate. Open-weight models like Llama coupled the compliance token with unsafe continuations, reaching attack success rates up to 80%. In contrast, the strongly aligned GPT-3.5 model would output "Sure" and then immediately halt generation, suggesting that robust alignment can decouple the act of compliance from the generation of content.

Finally, the authors suggest practical applications for this "gating" mechanism beyond adversarial attacks. Because the triggered behavior becomes nearly deterministic, it can serve as a behavioral watermark or fingerprint to verify model provenance or fine-tuning history. Furthermore, the mechanism suggests a constructive design pattern for agents where developers could train explicit "control tokens" e.g., , that force models into deterministic, auditable modes like JSON-only outputs for safer tool use.