Exploring Hidden Backdoors: How AI and Ghidra Identified Vulnerabilities in 40MB Binaries

Exploring AI’s Potential in Binary Malware Detection

In a groundbreaking study, we tested AI agents for malware detection in binary executables, shedding light on the potential and limitations of current technology.

🔍 Key Highlights:

• Partnership with Experts: Collaborating with Michał “Redford” Kowalczyk, we benchmarked AI agents in identifying hidden backdoors in binaries without source code access.
• Surprising Capabilities: AI can uncover hidden malicious code in small to mid-sized binaries but struggled with complex ones, achieving a detection rate of only 49% with notable false positives.
• Real-World Implications: Recent supply chain attacks signal that AI’s role in malware detection is crucial as threats evolve with high stakes in securing digital infrastructures.

💡 Current Findings:

• Benchmark Results: Access the complete findings on our open-source project at QuesmaOrg/BinaryAudit.
• Evolving Challenges: While AI aids initial security audits, more advancements are critical for practical applications in real-time environments.

👉 Join the Conversation! Share your thoughts on AI in cybersecurity in the comments below or connect with us to explore future innovations in this essential field!

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