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Technical University of Darmstadt
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LLMs can exploit syntactic patterns to falsely inflate detection rates in hardware security benchmarks, but a new obfuscation framework can slash their effectiveness by up to 78.6%.
Current AI security benchmarks are fundamentally flawed due to exploitability, staleness, and runtime variability, rendering their results unreliable.
AegisSat offers a defense-in-depth security framework that fortifies AI-enabled satellite systems against critical vulnerabilities, ensuring reliable and secure operation in the harsh space environment.
Achieve a 90% reduction in unsafe LLM generations with NeST, a parameter-efficient method that selectively tunes safety-relevant neurons, outperforming full fine-tuning and LoRA by orders of magnitude in parameter efficiency.
