Evaluating Cryptographic API Misuse Detectors for Go
- URL: http://arxiv.org/abs/2604.24085v1
- Date: Mon, 27 Apr 2026 06:19:56 GMT
- Title: Evaluating Cryptographic API Misuse Detectors for Go
- Abstract summary: This paper presents the first comprehensive study of cryptographic API misuse detection in Go.<n>We identify and analyze 4 state-of-the-art tools (CodeQL, Gopher, Gosec, and Snyk Code) and establish a consolidated taxonomy of 14 relevant misuse classes.<n>Through an experimental evaluation of 328 security-critical open-source Go projects, we discovered 7,473 cryptographic API misuses.
- Score: 5.939983212292006
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cryptographic API misuse represents a critical vulnerability class that undermines the security foundations of modern software. Yet, it remains largely unexplored in Go despite its dominance in security-critical infrastructure. This paper presents the first comprehensive study of cryptographic API misuse detection in Go, identifying and analyzing 4 state-of-the-art tools (CodeQL, Gopher, Gosec, and Snyk Code) and establishing a consolidated taxonomy of 14 relevant misuse classes. Through an experimental evaluation of 328 security-critical open-source Go projects, we discovered 7,473 cryptographic API misuses, providing insights into the prevalence and distribution of these vulnerabilities. Our systematic comparison reveals significant variations in misuse coverage, with immediate practical implications for security engineers and long-term implications for research in this domain.
Related papers
- Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale [54.83971397981572]
Language-model agents increasingly operate over complete software repositories.<n> cybersecurity evaluations measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.<n>We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
arXiv Detail & Related papers (2026-09-14T17:44:51Z) - ZKP Security Tools and Verification: Coverage, Effectiveness, Adoption, and Challenges [41.28438661234427]
Zero-knowledge proofs (ZKPs) have become a core technology for privacy and verifiable computing.<n>They are used to secure blockchains that handle billions of dollars and identity applications dealing with sensitive personal data.<n>Researchers and practitioners have developed a growing set of bug detection and formal verification methods to secure these systems.<n>Yet their real-world effectiveness and adoption remain unclear.
arXiv Detail & Related papers (2026-07-26T16:54:31Z) - From Theory to Practice: Code Generation Using LLMs for CAPEC and CWE Frameworks [2.7662026539843123]
We present a novel dataset that provides examples of vulnerable code snippets corresponding to CAPEC and CWE descriptions.<n>This dataset is a valuable resource for training machine learning models focused on automatic vulnerability detection and remediation.<n>The final dataset comprises 615 CAPEC code snippets in three programming languages: Java, Python, and JavaScript.
arXiv Detail & Related papers (2026-04-02T21:56:41Z) - "MCP Does Not Stand for Misuse Cryptography Protocol": Uncovering Cryptographic Misuse in Model Context Protocol at Scale [27.85822797774986]
The Model Context Protocol (MCP) is emerging as the interface for tool integration.<n>MCP provides no guarantees of authenticity or confidentiality, forcing developers to implement cryptography themselves.<n>We presentYSCOPE, the first domain-specific framework for detecting cryptographic misuses in MCP implementations.<n>Our study establishes the first ecosystem-wide view of cryptographic misuse in MCP and provides both tools and insights to strengthen the security foundations of this rapidly growing protocol.
arXiv Detail & Related papers (2025-12-03T13:25:59Z) - An Empirical Study on the Security Vulnerabilities of GPTs [48.12756684275687]
GPTs are one kind of customized AI agents based on OpenAI's large language models.<n>We present an empirical study on the security vulnerabilities of GPTs.
arXiv Detail & Related papers (2025-11-28T13:30:25Z) - Weakly Supervised Vulnerability Localization via Multiple Instance Learning [46.980136742826836]
We propose a novel approach called WAVES for WeAkly supervised Vulnerability localization via multiplE inStance learning.<n>WAVES has the capability to determine whether a function is vulnerable (i.e., vulnerability detection) and pinpoint the vulnerable statements.<n>Our approach achieves comparable performance in vulnerability detection and state-of-the-art performance in statement-level vulnerability localization.
arXiv Detail & Related papers (2025-09-14T15:11:39Z) - SAVANT: Vulnerability Detection in Application Dependencies through Semantic-Guided Reachability Analysis [6.989158266868967]
integration of open-source third-party library dependencies in Java development introduces significant security risks.<n>Savant combines semantic preprocessing with LLM-powered context analysis for accurate vulnerability detection.<n>Savant achieves 83.8% precision, 73.8% recall, 69.0% accuracy, and 78.5% F1-score, outperforming state-of-the-art SCA tools.
arXiv Detail & Related papers (2025-06-21T19:48:13Z) - CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale [45.97598662617568]
We introduce CyberGym, a large-scale benchmark featuring 1,507 real-world vulnerabilities across 188 software projects.<n>We show that CyberGym leads to the discovery of 35 zero-day vulnerabilities and 17 historically incomplete patches.<n>These results underscore that CyberGym is not only a robust benchmark for measuring AI's progress in cybersecurity but also a platform for creating direct, real-world security impact.
arXiv Detail & Related papers (2025-06-03T07:35:14Z) - Vulnerability Detection with Code Language Models: How Far Are We? [40.455600722638906]
PrimeVul is a new dataset for training and evaluating code LMs for vulnerability detection.
It incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks.
It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues.
arXiv Detail & Related papers (2024-03-27T14:34:29Z) - SliceLocator: Locating Vulnerable Statements with Graph-based Detectors [33.395068754566935]
SliceLocator identifies the most relevant taint flow by selecting the highest-weighted flow path from all potential vulnerability-triggering statements.<n>We demonstrate that SliceLocator consistently performs well on four state-of-the-art GNN-based vulnerability detectors.
arXiv Detail & Related papers (2024-01-05T10:15:04Z) - Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study [44.25093111430751]
In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars.
Various tools have been developed to detect and mitigate vulnerabilities in smart contracts.
This study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice.
arXiv Detail & Related papers (2023-12-27T11:26:26Z) - A Systematic Evaluation of Automated Tools for Side-Channel Vulnerabilities Detection in Cryptographic Libraries [6.826526973994114]
We surveyed the literature to build a classification of 34 side-channel detection frameworks.
We then built a benchmark of representative cryptographic operations on a selection of 5 promising detection tools.
We offer a classification of recently published side-channel vulnerabilities.
We find that existing tools can struggle to find vulnerabilities for a variety of reasons, mainly the lack of support for SIMD instructions, implicit flows, and internal secret generation.
arXiv Detail & Related papers (2023-10-12T09:18:26Z) - Detecting Misuse of Security APIs: A Systematic Review [5.329280109719902]
Security Application Programming Interfaces (APIs) are crucial for ensuring software security.<n>Their misuse introduces vulnerabilities, potentially leading to severe data breaches and substantial financial loss.<n>This study rigorously reviews the literature on detecting misuse of security APIs to gain a comprehensive understanding of this critical domain.
arXiv Detail & Related papers (2023-06-15T05:53:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.