arXiv subjects

Ehsan Firouzi

Publications and source records attributed to Ehsan Firouzi.

6 recordsLinked to original sources

arXiv2026

Persistent Human Feedback, LLMs, and Static Analyzers for Secure Code Generation and Vulnerability Detection

Existing literature heavily relies on static analysis tools to evaluate LLMs for secure code generation and vulnerability detection. We reviewed 1,080 LLM-generated code samples, built a human-validated ground-truth, and compared the outputs of two widely used static security tools, CodeQL and Semgrep, against this corpus. While 61% of the samples were genuinely secure, Semgrep and CodeQL classified 60% and 80% as secure, respectively. Despite the apparent agreement in aggregate statistics, per-sample analysis reveals substantial discrepancies: only 65% of Semgrep's and 61% of CodeQL's reports correctly matched the ground truth. These results question the reliability of static analysis tools as sole evaluators of code security and underscore the need for expert feedback. Building on this insight, we propose a conceptual framework that persistently stores human feedback in a dynamic retrieval-augmented generation pipeline, enabling LLMs to reuse past feedback for secure code generation and vulnerability detection.

cs.CR↗

arXiv2026

Can Developers rely on LLMs for Secure IaC Development?

We investigated the capabilities of GPT-4o and Gemini 2.0 Flash for secure Infrastructure as Code (IaC) development. For security smell detection, on the Stack Overflow dataset, which primarily contains small, simplified code snippets, the models detected at least 71% of security smells when prompted to analyze code from a security perspective (general prompt). With a guided prompt (adding clear, step-by-step instructions), this increased to 78%.In GitHub repositories, which contain complete, real-world project scripts, a general prompt was less effective, leaving more than half of the smells undetected. However, with the guided prompt, the models uncovered at least 67% of the smells. For secure code generation, we prompted LLMs with 89 vulnerable synthetic scenarios and observed that only 7% of the generated scripts were secure. Adding an explicit instruction to generate secure code increased GPT secure output rate to 17%, while Gemini changed little (8%). These results highlight the need for further research to improve LLMs' capabilities in assisting developers with secure IaC development.

cs.CR↗

arXiv2024

ChatGPT's Potential in Cryptography Misuse Detection: A Comparative Analysis with Static Analysis Tools

The correct adoption of cryptography APIs is challenging for mainstream developers, often resulting in widespread API misuse. Meanwhile, cryptography misuse detectors have demonstrated inconsistent performance and remain largely inaccessible to most developers. We investigated the extent to which ChatGPT can detect cryptography misuses and compared its performance with that of the state-of-the-art static analysis tools. Our investigation, mainly based on the CryptoAPI-Bench benchmark, demonstrated that ChatGPT is effective in identifying cryptography API misuses, and with the use of prompt engineering, it can even outperform leading static cryptography misuse detectors.

cs.CR↗

arXiv2024

From Struggle to Simplicity with a Usable and Secure API for Encryption in Java

Cryptography misuses are prevalent in the wild. Crypto APIs are hard to use for developers, and static analysis tools do not detect every misuse. We developed SafEncrypt, an API that streamlines encryption tasks for Java developers. It is built on top of the native Java Cryptography Architecture, and it shields developers from crypto complexities and erroneous low-level details. Experiments showed that SafEncrypt is suitable for developers with varying levels of experience.

cs.CR↗

arXiv2024

Time to Separate from StackOverflow and Match with ChatGPT for Encryption

Cryptography is known as a challenging topic for developers. We studied StackOverflow posts to identify the problems that developers encounter when using Java Cryptography Architecture (JCA) for symmetric encryption. We investigated security risks that are disseminated in these posts, and we examined whether ChatGPT helps avoid cryptography issues. We found that developers frequently struggle with key and IV generations, as well as padding. Security is a top concern among developers, but security issues are pervasive in code snippets. ChatGPT can effectively aid developers when they engage with it properly. Nevertheless, it does not substitute human expertise, and developers should remain alert.

cs.CR↗

arXiv2024

LLM Security Guard for Code

Many developers rely on Large Language Models (LLMs) to facilitate software development. Nevertheless, these models have exhibited limited capabilities in the security domain. We introduce LLMSecGuard, a framework to offer enhanced code security through the synergy between static code analyzers and LLMs. LLMSecGuard is open source and aims to equip developers with code solutions that are more secure than the code initially generated by LLMs. This framework also has a benchmarking feature, aimed at providing insights into the evolving security attributes of these models.

cs.SE↗