Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.32601

VulContextBench: A Benchmark for Security Context Retrieval in Coding Agents

Yikun Li·Jinfeng Jiang·Yuheng Yieh·Ting Zhang·Yide Yin·Wen Bin Leow·Eng Lieh Ouh·Lwin Khin Shar·David Lo
View original source ↗Read source document ↗Export metadata ↓

Abstract

Vulnerability-detection benchmarks score the verdict an agent reaches, not the evidence it gathered. A model that recalls a CVE from pretraining therefore scores the same as one that traced the data flow. We study a task where this difference matters, deciding whether a commit introduces a vulnerability. Instead of scoring the verdict, we score whether the agent retrieved the code its conclusion depends on. We present VulContextBench, a benchmark of 111 vulnerability-introducing commits (VICs) across 83 repositories, 63 CWEs, and five languages. Existing datasets label such commits by tracing a fix back through the version history, which often points to the wrong commit. We therefore audit every case by hand against an explicit four-criterion definition of a VIC, so the benchmark does not inherit that label noise. Each case is annotated with gold context, 464 code blocks in total, each tagged by its role in the evidence for the vulnerability. We evaluate seven frontier models with precision, recall and F1 at three granularities (file, block, and line), scored separately on the context an agent viewed while exploring and on the context it finally declared as evidence. The gap between the two is the main finding. Every model opens most of the gold context while exploring, but reports only part of it as evidence. At the level of code blocks, the share of the gold context a model reports is 37 to 73 percentage points below the share it viewed. Qwen3-Coder-Next views 86.3% of the lines in annotated code blocks but cites only 12.9% in its final report. GPT-5.5, which cites the most, views 73% and reports 36%. These results highlight a gap between finding relevant code and selecting it for the final report, which verdict-level benchmarks cannot reveal.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yikun Li, Jinfeng Jiang, Yuheng Yieh, Ting Zhang, Yide Yin, Wen Bin Leow, Eng Lieh Ouh, Lwin Khin Shar, David Lo. 2026-09-26. VulContextBench: A Benchmark for Security Context Retrieval in Coding Agents. https://arxiv.org/abs/2609.32601

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant information from external knowledge bases to provide more accurate, contextually informed, and up-to-date responses. However, this reliance on external knowledge introduces significant security vulnerabilities, as many RAG systems (e.g., Google Search) rely on large and unsanitized data repositories (e.g., Reddit). In this paper, we unveil a novel threat in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base. When a user's query contains attacker-specified trigger words, the RAG retrieves and refers to these malicious passages, enabling the attacker to steer the response without altering the user input or modifying the RAG weights. BadRAG operates in two phases: (i) malicious passages are optimized to be retrieved exclusively when trigger words appear in user queries; (ii) these passages are meticulously crafted to achieve adversarial generation objectives, including denial of service, sentiment manipulation, context leakage, and tool misuse. Our experiments show that injecting just 10 malicious passages (0.04\% of the external corpora) achieves a 98.2\% retrieval success rate and increases negative response rates from 0.22\% to 72\% for queries containing triggers.

cs.CR↗
arXiv2026

Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation

Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses. This survey systematically reviews GAN-based adversarial defenses in cybersecurity (2021--August 31, 2025), consolidating recent progress, identifying gaps, and outlining future directions. Using a PRISMA-compliant systematic literature review protocol, we searched five major digital libraries. From 829 initial records, 185 peer-reviewed studies were retained and synthesized through quantitative trend analysis and thematic taxonomy development. We introduce a four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model. GANs improve detection accuracy, robustness, and data utility across network intrusion detection, malware analysis, and IoT security. Notable advances include WGAN-GP for stable training, CGANs for targeted synthesis, and hybrid GAN models for improved resilience. Yet, persistent challenges remain such as instability in training, lack of standardized benchmarks, high computational cost, and limited explainability. GAN-based defenses demonstrate strong potential but require advances in stable architectures, benchmarking, transparency, and deployment. We propose a roadmap emphasizing hybrid models, unified evaluation, real-world integration, and defenses against emerging threats such as LLM-driven cyberattacks. This survey establishes the foundation for scalable, trustworthy, and adaptive GAN-powered defenses.

cs.CR↗
arXiv2026

NonTextual Target Attack

Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with predefined target responses. However, restricting the objective as inducing fixed targets inherently constrains the adversarial search space, limiting the overall attack efficacy. Furthermore, existing methods typically require numerous optimization iterations to fulfill the large gap between the fixed target and the original LLM output, resulting in low attack efficiency. To overcome these limitations, we propose NonTextual Target Attack (NTA),the first gradient-based attack that relies on a non-textual constrained objective to maximize the unsafety probability of the LLM output, without enforcing any response patterns. For tractable optimization, we further decompose this objective into two constrained sub-objectives, which can be approximated by two differentiable unconstrained losses, to iteratively optimize the response and the adversarial prompt in the neighborhood of the original prompt, with a theoretical analysis to validate the decomposition. In contrast to existing attacks, NTA first realizes gradient-based prompt optimization on a non-textual target and significantly expands the attack space, enabling more flexible and efficient exploration of LLM vulnerabilities. Extensive evaluations show that NTA achieves an average attack success rate of 96.8% against recent safety-aligned LLMs with only 100 optimization iterations on AdvBench, outperforming state-of-the-art gradient-based attacks by over 40%.

cs.CR↗