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Unlocking interpretable uncertainty in LLMs by mapping semantic doubt and certainty anchors to a low-dimensional subspace, outperforming baselines even when controlling for output length.
FloatDoor reveals that LLMs can be covertly compromised to exhibit malicious behavior on specific platforms while appearing benign elsewhere, exposing a critical security gap in AI deployment.
Ignoring prompt knowledge is a critical security flaw, as LLMs can covertly transmit hidden messages through their deterministic sampling processes.
LLM-powered agents can autonomously generate fuzz harnesses for Java libraries, outperforming existing automated approaches and even uncovering bugs in well-fuzzed code.
