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CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection

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arxiv 2503.09433 v2 pith:RGHNX25V submitted 2025-03-12 cs.CR cs.AIcs.SE

CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection

Richard A. Dubniczky , Krisztofer Zolt\'an Horv\'at , Tam\'as Bisztray , Mohamed Amine Ferrag , Lucas C. Cordeiro , Norbert Tihanyi This is my paper
classification cs.CR cs.AIcs.SE
keywords codecastledatasetllmsstaticvulnerabilitiesanalysisformal
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Identifying vulnerabilities in source code is crucial, especially in critical software components. Existing methods such as static analysis, dynamic analysis, formal verification, and recently Large Language Models are widely used to detect security flaws. This paper introduces CASTLE (CWE Automated Security Testing and Low-Level Evaluation), a benchmarking framework for evaluating the vulnerability detection capabilities of different methods. We assess 13 static analysis tools, 10 LLMs, and 2 formal verification tools using a hand-crafted dataset of 250 micro-benchmark programs covering 25 common CWEs. We propose the CASTLE Score, a novel evaluation metric to ensure fair comparison. Our results reveal key differences: ESBMC (a formal verification tool) minimizes false positives but struggles with vulnerabilities beyond model checking, such as weak cryptography or SQL injection. Static analyzers suffer from high false positives, increasing manual validation efforts for developers. LLMs perform exceptionally well in the CASTLE dataset when identifying vulnerabilities in small code snippets. However, their accuracy declines, and hallucinations increase as the code size grows. These results suggest that LLMs could play a pivotal role in future security solutions, particularly within code completion frameworks, where they can provide real-time guidance to prevent vulnerabilities. The dataset is accessible at https://github.com/CASTLE-Benchmark.

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Cited by 6 Pith papers

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    cs.CR 2026-04 unverdicted novelty 7.0

    RealVuln benchmark finds security-specialized scanners outperform general-purpose LLMs and rule-based SAST tools on hand-labeled vulnerable Python code under F3 scoring, with all artifacts released.

  2. SecDrift: Measuring Sector-Conditioned Security Drift in AI-Generated Code

    cs.CR 2026-07 accept novelty 6.0

    Sector-conditioned prompt framing does not significantly change static-analysis vulnerability rates in AI-generated code; the apparent effect is a composition artifact of two CWE categories.

  3. Teaching LLMs Program Semantics via Symbolic Execution Traces

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    Training Qwen3-8B on symbolic execution traces from Soteria improves violation detection in C programs by over 17 points, transfers across five property types, and shows superadditive gains with chain-of-thought.

  4. Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software

    cs.CR 2026-02 reject novelty 5.0

    Frontend features of LLM-generated apps can predict hidden backend vulnerabilities that a given model tends to reproduce, enabling black-box attack triage.

  5. ATLAS: Multi-View Code Representation Tool for C and C++ Source Programs

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