PoVSmith automates PoV test generation for library vulnerabilities in apps via call paths and LLM feedback, correctly identifying 96% of entry points and producing effective attack tests in 55% of 33 evaluated Java pairs.
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SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
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Generating Proof-of-Vulnerability Tests to Help Enhance the Security of Complex Software
PoVSmith automates PoV test generation for library vulnerabilities in apps via call paths and LLM feedback, correctly identifying 96% of entry points and producing effective attack tests in 55% of 33 evaluated Java pairs.
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SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.