Towards stack buffer overflow detection in stripped binaries with context-augmented LLMs
TL;DR
This work introduces a static, decompiler-driven pipeline built on top of Ghidra that augments decompiled functions with binary-derived evidence including recovered stack regions, callgraph context, and p-code-derived features and presents the real-world evaluation as a diagnostic stress test rather than evidence of a deployable detector.
Abstract
The detection of vulnerabilities in stripped binaries remains a challenge in software security because stripping removes many of the contextual signals that both humans and automated systems use to identify and validate unsafe behavior. Detecting stack buffer overflows is particularly challenging: symbols are gone, types are absent, stack objects are poorly represented, and the decompiler output that replaces them is often noisy, incomplete, or incorrect. Prior work has applied LLMs to vulnerability analysis in binaries, but typically over decompiler output alone or with limited additional context. In this work, we take the position that the central problem is not whether an LLM can read decompiled code, but whether it can be given enough recovered program context to reason effectively about a stripped binary. We therefore introduce a static, decompiler-driven pipeline built on top of Ghidra that augments decompiled functions with binary-derived evidence including recovered stack regions, callgraph context, and p-code-derived features. This pipeline applies LLM agents in three progressively narrower stages of analysis designed to separate broad screening from expensive confirmation. We evaluate this pipeline using gpt-oss-120b [1] on a synthetic dataset compiled from a subset of NIST’s Juliet [2] dataset and stress-test the pipeline using both gpt-oss-120b and gpt-5.4 on paired vulnerable and patched real-world binaries: 12 CVE pairs published after the model’s training cutoff and 10 pre-cutoff pairs. The Juliet results show that context-augmented, staged LLM analysis can identify stack buffer overflow behavior in stripped binaries under controlled conditions. On real-world binaries, candidate generation usually surfaces the known-vulnerable target but at a high discovery burden, and final targeted reachability remains low; we therefore present the real-world evaluation as a diagnostic stress test rather than evidence of a deployable detector.
