Brian Crawford
4 indexed papers
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The paper introduces an adversarial technique using genetic algorithms to deceive LLM-powered software reverse engineering agents, demonstrating that attackers can corrupt the analytical output of these systems by exploiting prompt injection vulnerabilities.
The paper introduces an adversarial technique using genetic algorithms to deceive LLM-powered software reverse engineering agents, demonstrating that attackers can corrupt the analytical output of automated binary analysis systems.
This paper investigates prompt injection attacks targeting software reverse engineering AI agents, demonstrating detection and defense strategies against both direct and obfuscated attacks.
This paper investigates prompt injection attacks targeting software reverse engineering AI agents, demonstrating detection and defense strategies against both direct and obfuscated attacks.
Papers
Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents
This paper investigates prompt injection attacks targeting software reverse engineering AI agents, demonstrating detection and defense strategies against both direct and obfuscated attacks.