arXiv:2605.30667v1 Announce Type: cross Abstract: Software tools for reverse engineering executable binary files, such as Ghidra, enable malware analysts to safely conduct robust static analysis without having access to original source code. Coupled with the analytic power of large language models (LLM), agentic systems enabled with tools, such as GhidraMCP, can allow analysts to automate a previously human driven process. Although this automation can increase the productivity of a single malware analyst, it also introduces a new area of vulnerability for malware obfuscation. This paper presen
The proliferation of AI agents in sensitive domains like cybersecurity is rapidly creating new attack surfaces, making defensive innovation against adversarial AI paramount.
This highlights the immediate need for robust security measures in AI-powered tools, especially those that automate critical human tasks, to prevent exploitation by malicious actors.
The risk calculus for deploying AI agents in cybersecurity shifts, requiring proactive development of attack and defense strategies tailored to AI-enabled reverse engineering tools.
- · AI security researchers
- · Cybersecurity firms specializing in AI defense
- · Robust AI agent platforms
- · Developers of insecure AI agents
- · Organizations relying solely on traditional malware detection
- · AI-enabled reverse engineering tool users without robust defenses
Malware authors will leverage new obfuscation techniques to specifically target AI-powered reverse engineering tools.
An arms race will intensify between AI-driven cybersecurity defenses and adversarial AI techniques.
The development of 'red-teaming' AI agents to test the resilience of other AI systems becomes a critical component of software development lifecycles.
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Read at arXiv cs.AI