[Paper] How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test

Source: arXiv - 2601.07084v1

Overview

The paper “How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test” puts the latest “secure‑code‑generation” tricks—fine‑tuning for vulnerability awareness, prefix‑tuning, and prompt‑optimisation—under a realistic, adversarial microscope. By injecting everyday prompt variations (paraphrases, cue flips, extra context) the authors discover that many of these defenses crumble, exposing a gap between reported security and what actually works in practice.

Key Contributions

• First systematic adversarial audit of three state‑of‑the‑art secure code generators (SVEN, SafeCoder, PromSec).
• Unified evaluation pipeline that jointly measures security (static analysis, vulnerability scanners) and functionality (executable test suites) on the same generated snippets.
• Empirical evidence that static analyzers dramatically over‑estimate safety (by 7–21×) and that 37–60 % of “secure” outputs are non‑functional.
• Robustness breakdown under adversarial prompts: true “secure + functional” rates drop from ~70 % (clean prompts) to 3–17 %.
• Actionable best‑practice checklist for building and evaluating resilient secure‑code‑generation pipelines.
• Open‑source release of the benchmark, attack scripts, and evaluation harness.

Methodology

1. Targeted systems – The authors re‑implemented the three published defenses (SVEN, SafeCoder, PromSec) using the authors’ released models and prompts.
2. Adversarial prompt suite – They crafted realistic perturbations that a developer might unintentionally introduce or an attacker could exploit:
   • Paraphrasing: re‑wording the same request with synonyms or different sentence structures.
   • Cue inversion: swapping “secure”/“unsafe” keywords, flipping “do not” statements, or moving security hints to later parts of the prompt.
   • Context manipulation: adding unrelated code/comments, inserting noisy boilerplate, or changing surrounding documentation.
3. Unified test harness – For each generated snippet the pipeline runs:
   • Static security analysis (multiple open‑source scanners) to flag known CWE patterns.
   • Dynamic functional tests (unit‑test style harnesses) to verify the code actually compiles/runs and meets the functional spec.
   • Combined metric: a result is counted as “secure‑and‑functional” only if it passes both checks.
4. Baseline vs. adversarial comparison – The same prompts are evaluated in their clean form and under each adversarial transformation, allowing a direct robustness measurement.

Results & Findings

• Static analyzers are overly optimistic: they label up to 21× more snippets as “secure” than the combined functional check does.
• Functionality loss: 37–60 % of code that passes the security scanner fails to compile or run the intended test.
• Adversarial fragility: Even minor prompt tweaks cause the secure‑and‑functional rate to collapse to single‑digit percentages.
• No single defense dominates – all three methods exhibit similar vulnerability patterns, suggesting a systemic issue rather than a model‑specific bug.

Practical Implications

• Don’t trust security‑only metrics – If you integrate a “secure code generation” model into CI/CD, pair its output with both static analysis and automated functional tests before deployment.
• Prompt hygiene matters – Small wording changes can bypass defenses. Teams should standardise prompt templates and possibly sanitise user‑provided prompts before feeding them to the model.
• Model‑level hardening is insufficient – The findings encourage developers to treat LLM‑generated code as assistive rather than authoritative for security‑critical components.
• Tooling roadmap – The released benchmark can become a regression suite for any new secure‑code‑generation technique, ensuring future models are evaluated under realistic adversarial conditions.
• Risk assessment – Companies can quantify the residual risk of using LLM‑generated code by applying the paper’s combined security + functionality metric rather than relying on static scans alone.

Limitations & Future Work

• Scope of languages – The study focuses on a handful of popular languages (Python, JavaScript, Java). Extending to systems languages (C/C++) may reveal different failure modes.
• Adversary model – Prompt perturbations are realistic but still handcrafted; automated adversarial generation (e.g., gradient‑based prompt attacks) could uncover even more subtle weaknesses.
• Static analyzer diversity – While multiple scanners were used, none are perfect; false negatives in the security check could still mask vulnerabilities.
• Model updates – The evaluated defenses are based on static releases; continual model fine‑tuning could shift robustness, so ongoing benchmarking is needed.

Bottom line: Secure code generation is promising, but developers must treat LLM outputs as candidate code, validate them end‑to‑end, and adopt the paper’s best‑practice checklist to avoid a false sense of security. The authors’ open‑source suite makes it easier for the community to hold future models accountable.

Authors

• Melissa Tessa
• Iyiola E. Olatunji
• Aicha War
• Jacques Klein
• Tegawendé F. Bissyandé

Paper Information

• arXiv ID: 2601.07084v1
• Categories: cs.CR, cs.SE
• Published: January 11, 2026
• PDF: Download PDF