Rozhin Yasaei
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This paper introduces an agentic LLM-driven framework that automates the generation of functionally correct and security-relevant hardware netlist obfuscation for protecting intellectual property.
The paper introduces the Safety Asymmetry Score (SAS) to measure how a model's susceptibility to adversarial attacks changes based on whether the malicious content arrives via the user message, tool metadata, or tool output, revealing systematic, channel-dependent blind spots.
The paper introduces the Safety Asymmetry Score (SAS) to measure how a model's vulnerability to adversarial content changes based on whether the malicious input arrives via the user message, tool metadata, or tool output, revealing systematic, channel-dependent blind spots.
Papers
Same Payload, Different Channel: Measuring Trust Asymmetry in Tool-Using Language Models
The paper introduces the Safety Asymmetry Score (SAS) to measure how a model's susceptibility to adversarial attacks changes based on whether the malicious content arrives via the user message, tool m…