arXiv:2606.20508v1 Announce Type: cross Abstract: Prior work has shown that in-context demonstrations can jailbreak language models, but it remains unclear how models interpret different types of compliance demonstrations. We study this by mixing benign compliance demonstrations (non-harmful request, helpful response) with harmful compliance demonstrations (harmful request, helpful response) and testing three hypotheses about how demonstration composition drives harmful compliance. Across four models, we find that benign and harmful demonstrations are not interchangeable: benign demonstrations
The rapid advancement and deployment of large language models necessitate a deeper understanding of their safety alignment and vulnerability to adversarial actions, particularly as they integrate into critical applications.
Understanding how LLMs learn from mixed compliance demonstrations is crucial for developing robust safety mechanisms and preventing unintended harmful behaviors, which directly impacts trust and regulatory frameworks.
This research provides a more nuanced view of LLM alignment, differentiating how models process benign versus harmful compliance examples, indicating that safety alignment is not a monolithic property.
- · AI safety researchers
- · Developers of secure LLM applications
- · Organizations prioritizing AI ethics
- · Malicious actors exploiting LLM vulnerabilities
- · LLM developers ignoring subtle alignment issues
- · End-users exposed to unaligned AI
Developers will begin to implement more sophisticated training and evaluation protocols for LLM safety.
This improved understanding will lead to the development of new techniques for making LLMs more resistant to 'jailbreaking' attacks.
Increased transparency and control over LLM behavior could accelerate regulatory adoption and public acceptance of advanced AI systems.
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Read at arXiv cs.LG