VulBinLLM: Using Large Language Models to Unlock Vulnerabilities Hidden in Stripped Binaries

A team of researchers from UCLA and Cisco Research has unveiled a framework called VulBinLLM, marking a major leap forward in binary vulnerability detection. In a field long dominated by human experts and limited automation, this system leverages the power of large language models (LLMs) to analyze stripped binaries—compiled software without debugging symbols or metadata.

“Recognizing vulnerabilities in stripped binary files presents a significant challenge in software security… effectively and scalably detecting vulnerabilities within these binary files is still an open problem,” the researchers explain.

VulBinLLM is an LLM-assisted static analysis framework that mimics traditional reverse engineering workflows while enhancing them with powerful prompt engineering, extended memory, and neural decompilation techniques. Its goal? To detect Common Weakness Enumeration (CWE) vulnerabilities in stripped binaries, where source-level context is lost.

“Vul-BinLLM is a LLM-powered binary analysis framework… the first framework that focuses on recovering syntactic information to highlight vulnerable features using LLMs.”

Unlike typical decompilers, VulBinLLM does more than translate machine code—it optimizes the decompiled output with syntactic and semantic enhancements that make security flaws stand out for LLMs. Comments, variable names, and control structures are reconstructed and refined using GPT-4o, enabling more effective downstream vulnerability reasoning.

The process begins by feeding stripped binaries into tools like Ghidra and RetDec. The code is decompiled and passed through VulBinLLM’s enhancement pipeline:

• Vulnerability annotation: Adds inline comments about weak spots like buffer overflows and command injection.
• Prompt engineering: Uses in-context learning and chain-of-thought (CoT) reasoning to guide LLMs through complex vulnerability analysis.
• Memory management: Stores past function analyses in a shared database to simulate extended context windows—overcoming the token limit limitations of current LLMs.

“This recovered source code… is optimized specifically for vulnerability detection, with embedding the key features and potential security flaws highlighted for LLMs to focus on.”

To benchmark VulBinLLM, the team compiled over 20,000 test binaries from the Juliet C/C++ dataset, stripping all debug symbols to simulate real-world binaries. They then tested VulBinLLM’s accuracy against LATTE, a state-of-the-art taint analysis LLM system.

In the task of detecting CWE-78 (OS Command Injection), VulBinLLM achieved perfect recall (100%) with a precision of 84.67%, outperforming LATTE’s perfect precision but lower recall. For other classes like CWE-134 and CWE-190, VulBinLLM approached 99% accuracy, far ahead of LATTE in some categories.

“Our evaluations show that Vul-BinLLM is highly effective in detecting vulnerabilities on the compiled Juliet dataset.”

Stripped binaries are ubiquitous in commercial software, firmware, and third-party libraries—yet notoriously difficult to analyze due to lost metadata. With VulBinLLM, the cybersecurity community now has a scalable, automated, and LLM-driven tool that bridges the gap between high-level reasoning and low-level binary code.

“Binary reverse engineering continues to be a crucial component of software vulnerability discovery, relying heavily on a combination of human skill and machine assistance.”

The authors note limitations—such as the scarcity of real-world binary vulnerability datasets—and suggest future enhancements, including direct assembly-level analysis, formal vulnerability classification for binaries, and retrieval-augmented generation (RAG) systems for code inspection.

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