AI vs. Traditional Static Analysis: Evaluating Buffer Overflow Detection Capabilities

Buffer overflow vulnerabilities remain a persistent challenge in software security, with detection methods evolving alongside advancements in artificial intelligence. Recent studies compare the effectiveness of AI-driven tools against traditional static analyzers, revealing trade-offs in recall rates, false positives, and integration workflows. This analysis focuses on empirical data from industry and academic research to assess the current state of vulnerability detection.

Performance Metrics: AI vs. Static Analysis

AI models, particularly large language models (LLMs) and graph neural networks (GNNs), demonstrate superior recall rates for buffer overflow detection. According to NCC Group’s 2024 study, AI achieves 82% recall in Juliet test cases, outperforming static analyzers like Cppcheck and CodeQL, which range between 60-70%¹. However, this comes with a 30% false positive rate, nearly double that of traditional tools. Static analyzers maintain an advantage in precision, with false positives limited to 10-15%².

Metric	AI Models	Static Analyzers
Buffer Overflow Detection	82% recall	60-70% recall
False Positives	30%	10-15%
Integration	IDE plugins (Copilot, Aikido)	CI/CD pipelines

Hybrid Approaches and Emerging Solutions

Combining AI with static analysis shows promise in mitigating weaknesses of both methods. Prompt engineering reduces false positives by 40% when used alongside traditional tools¹. Specialized models like Vul-Mixer achieve 98.3% accuracy with minimal computational overhead, using just 0.2% of CodeBERT’s parameters³. Industry tools such as Aikido AutoFix and GitHub CodeQL now incorporate sandboxed LLMs for secure remediation suggestions directly within development environments.

Dataset Limitations and Real-World Challenges

Current training datasets present significant obstacles for both approaches. The Juliet 1.3 benchmark contains 64,099 C/C++ test cases but suffers from 58.3% test-train overlap, potentially inflating performance metrics⁴. Real-world datasets like Big-Vul often include non-compilable functions, while Draper VDISC contains 26.88% near-duplicate samples. Label accuracy remains problematic, with 70% of GitHub vulnerability labels being inaccurate according to Chakraborty et al.⁴.

Practical Considerations for Security Teams

Security professionals must weigh several factors when selecting detection methods. AI models require substantial computational resources and may struggle with large codebases, while static analyzers integrate more seamlessly with existing CI/CD pipelines. The financial impact of false positives becomes significant at scale, with some studies showing LLMs resolve less than 5% of real-world issues despite their detection capabilities⁴.

For teams implementing these tools, we recommend:

• Prioritizing high-confidence findings from AI tools for manual review
• Using static analyzers as a first pass in CI pipelines
• Considering hybrid tools like CodeQL that combine both approaches

Future Directions in Vulnerability Detection

Research indicates three key development areas: specialized models targeting specific vulnerability classes, improved datasets through industry-academia collaboration, and lightweight AI implementations for broader adoption. Meta’s Purple LLama CyberSecEval represents progress toward standardized benchmarks⁴. As tools evolve, the gap between academic research and industry requirements continues to narrow, particularly for high-impact vulnerabilities like buffer overflows.

The choice between AI and traditional methods ultimately depends on organizational needs, with hybrid solutions offering a balanced approach. Continued refinement of training data and model architectures will likely reduce current limitations, making AI-assisted detection increasingly viable for production environments.

References

1. NCC Group. (2024). Comparing AI vs. Static Analysis Tools.
2. Steenhoek et al. (2023). Evaluation of AI-Based Vulnerability Detection. ICSE.
3. Grahn, D. J. (2025). Vul-Mixer: Efficient ML-Assisted Detection. Wright State University.
4. Wang et al. (2021). PatchDB: Security Patch Dataset. IEEE DSN.