arXiv:2606.29646v1 Announce Type: new Abstract: Sleeper agents are the canonical model organism of deception: models trained to behave normally but to emit an unsafe behaviour on a specific trigger. Eliciting that behaviour without knowing the trigger has not been studied systematically. We study fuzzing: injecting Gaussian noise into a model's weights or residual-stream activations and checking whether the perturbed outputs reveal the behaviour. On 6 backdoored models (7B-13B) we compare both forms of fuzzing head-to-head against temperature-sampling baselines. Fuzzing elicits the hidden beha
The rapid advancement and deployment of Large Language Models (LLMs) necessitate immediate research into their safety and the detection of hidden, potentially malicious behaviors, especially as they become more integrated into critical systems.
The discovery and mitigation of 'sleeper agent' behaviors in LLMs are critical for ensuring their safety, trustworthiness, and preventing their misuse in sensitive applications.
The ability to systematically fuzz LLMs to detect hidden behaviors provides a new methodology for auditing AI safety and could lead to more robust model development and deployment practices.
- · AI safety researchers
- · Organizations deploying LLMs
- · Cybersecurity firms
- · Malicious AI developers
- · Black-box AI models
- · Organizations with inadequate AI auditing practices
Systematic methods emerge for identifying latent risks in deployed AI models.
Increased regulatory focus on AI model transparency and auditable safety features for LLMs.
The development of 'AI safety as a service' industries specializing in model interrogation and risk mitigation.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG