DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents

Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but insecure. Progress in secure code generation has been hindered by benchmarks that are small, non-executable, leak mitigation details, or rely on noisy analyzers and subjective judgments, making it difficult to measure whether security improves without sacrificing correctness. We address these gaps with CodeSecEval, an execution-based benchmark for secure code generation, comprising 255 Python tasks spanning 77 CWE categories. Each task provides paired insecure and secure implementations together with executable functional and vulnerability-targeted security tests, enabling precise and reproducible evaluation of secure code generation and insecure-code repair. Building on CodeSecEval, we propose SecAwareCoder, an agent-based framework that shifts code generation toward secure-by-construction synthesis. SecAwareCoder performs task-adaptive threat modeling to identify security-sensitive regions and derive task-grounded vulnerability hypotheses, uses these hypotheses to guide both constraint-aware code generation and security-aware test synthesis, and leverages execution feedback for targeted refinement. Experiments across multiple LLM backbones show that SecAwareCoder consistently improves Pass@1 and security robustness over prompting and analyzer-driven baselines, narrowing the security--correctness gap in LLM code generation.

Large Language Models (LLMs) can generate functionally correct code that remains insecure. Security-focused fine-tuning can be expensive and is not always available, motivating inference-time approaches. Security-oriented prompting and retrieval can reduce vulnerabilities, but may also remove required behavior or apply defenses incompatible with the task. The challenge is to identify relevant security knowledge and use it to improve security without sacrificing functionality. We present SecGent, an inference-time framework that retrieves weakness context from the Common Weakness Enumeration (CWE) database and applies it to secure code generation. By connecting task specifications to relevant textual CWE records, SecGent provides task-specific security guidance without relying on retrieved vulnerable or patched code examples. Complementary functional and security tests check the resulting implementation, and their execution feedback guides repairs toward satisfying both objectives. We evaluate SecGent with four open-source LLMs and Claude Sonnet 5.5 across five programming languages on the CWEval benchmark. It achieves the highest joint functional--security success against four inference-time baselines for all five models. Across the open-weight models, mean functional-security@1 (FS@1) improves by 15.5% over RAG, with gains in both functionality and security, and by 19.8% over the strongest prompt optimizer compiled for each model. On Sonnet, FS@1 reaches 92.1%, outperforming the strongest baseline AutoGen by 4.8%. In a separate comparison, SecGent outperforms Claude Code with security-focused prompting by over 6% in FS@1. Feedback-guided repair further improves FS@1 by up to 6.7% over generation without feedback, primarily by recovering functionality while checking security.

Large language models (LLMs) are increasingly used for code generation, but they struggle to generate functional code free of security vulnerabilities. Prior work to improve the secure code generation abilities of such coding LLMs has largely focused on evaluating code functionality and security separately using different datasets, or focused on finding vulnerabilities post-generation. At the same time, the text-generation domain has seen significant work on alignment techniques, where models are tuned such that their outputs exhibit certain qualities (e.g., helpfulness, harmlessness). Of particular interest is task-vector arithmetic, where linear operations on LLM weights can be used to arbitrarily enhance alignment while incurring only minimal computational overhead. We develop a novel method, SecVecCoder, leveraging task vectors to produce trustworthy code that is simultaneously functional and secure without the need for post-generation adjustment. Across six coding LLMs from three families on the CodeGuard+ benchmark, SecVecCoder improves the rate of trustworthy code completions by 2.1-36.0 percentage points over the base model, with improvements on unseen CWE types reaching up to 39.1 percentage points. Since the effectiveness of the coding LLM relies only on changing the model weights, SecVecCoder requires no method-specific decoding and hence achieves a decoding latency within 0.6% of the base model's, on average.