arXiv:2606.17398v1 Announce Type: cross Abstract: Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains inherently challenging due to the irreversible loss of semantic information during compilation. Recent advances in machine learning, large language models (LLMs), and agentic AI systems have accelerated the adoption of AI-augmented binary reversing. Yet, the resulting body of work has become increasingly fragmented across reversing domains, artifact representations, learning approaches, and evalua
The rapid advancements in LLMs and agentic AI systems have reached a point where their application to complex, data-rich problems like binary reversing is becoming increasingly viable and necessary.
AI-augmented binary reversing addresses a foundational challenge in cybersecurity and software integrity, directly impacting vulnerability discovery, malware analysis, and the security of critical infrastructure.
The adoption of AI tools will significantly accelerate and improve the efficiency of binary analysis, fundamentally altering the workflows for reverse engineers and security researchers.
- · Cybersecurity firms
- · AI software developers
- · National security agencies
- · Software forensics teams
- · Malware developers
- · Attackers relying on zero-day exploits
- · Human-only reverse engineering firms
Increased efficiency and accuracy in identifying software vulnerabilities and analyzing malicious code.
A potential reduction in the time needed to patch critical software flaws, improving overall system resilience.
An acceleration of offensive and defensive cybersecurity capabilities, leading to an arms race in AI-driven tools for both sides.
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Read at arXiv cs.AI