# Activating Latent Security Knowledge through LLM-Guided Risk Analysis for Secure Code Generation

arXiv: https://arxiv.org/abs/2606.16244 · HTML: https://mlsanity.com/paper/2606.16244/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Builds on (most cited first, 22 of 22)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Is Self-Repair a Silver Bullet for Code Generation?](https://mlsanity.com/paper/2306.09896/) (2023)
- [Large Language Models for Code: Security Hardening and Adversarial Testing](https://mlsanity.com/paper/2302.05319/) (2023)
- [LoRA vs Full Fine-tuning: An Illusion of Equivalence](https://mlsanity.com/paper/2410.21228/) (2024)
- [How Secure is Code Generated by ChatGPT?](https://mlsanity.com/paper/2304.09655/) (2023)
- [Instruction Tuning for Secure Code Generation](https://mlsanity.com/paper/2402.09497/) (2024)
- [LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluations](https://mlsanity.com/paper/2303.09384/) (2023)
- [CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation](https://mlsanity.com/paper/2501.08200/) (2025)
- [CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models](https://mlsanity.com/paper/2408.01605/) (2024)
- [BaxBench: Can LLMs Generate Correct and Secure Backends?](https://mlsanity.com/paper/2502.11844/) (2025)
- [PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)](https://mlsanity.com/paper/2409.12699/) (2024)
- [Give LLMs a Security Course: Securing Retrieval-Augmented Code Generation via Knowledge Injection](https://mlsanity.com/paper/2504.16429/) (2025)
- [Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industry](https://mlsanity.com/paper/2601.18844/) (2026)
- [Persistent Human Feedback, LLMs, and Static Analyzers for Secure Code Generation and Vulnerability Detection](https://mlsanity.com/paper/2602.05868/) (2026)
- [GoodVibe: Security-by-Vibe for LLM-Based Code Generation](https://mlsanity.com/paper/2602.10778/) (2026)
- [Towards Effective Complementary Security Analysis using Large Language Models](https://mlsanity.com/paper/2506.16899/) (2025)
- [Autoregressive, Yet Revisable: In Decoding Revision for Secure Code Generation](https://mlsanity.com/paper/2602.01187/) (2026)

## Abstract (arXiv)

Large language models are pretrained on extensive software and security corpora, yet they frequently generate functionally correct code containing well-known vulnerabilities. Existing defenses commonly treat this behavior as a knowledge deficit, addressing it through model-specific fine-tuning or retrieval from large collections of vulnerability code examples. We argue that insecure generation can arise from a failure to activate task-relevant security knowledge, rather than from knowledge absence alone. We present BRACE, an inference-time, training-free security harness for risk-conditioned activation of security knowledge in black-box code generation. Given only a coding task, BRACE first utilizes an LLM (as a security expert) to identify task-relevant security risks. The predicted risk identifiers are validated against a lightweight canonical catalog and converted into concise, task-specific risk cues. BRACE then supplies these cues to the target code model, prompting it to apply its own secure-coding knowledge while satisfying the original functional requirements. The framework requires no target-model fine-tuning, parameter access, hidden states, mutable logits, learned retriever, or coding-example knowledge base. We evaluate BRACE on six open-weight models and six frontier commercial models with four benchmarks (CyberNative, HumanEval, CWEval, BaxBench), measuring functional correctness, security, joint functionality-security, and project-level generation. BRACE raises the average Safe Code Rate from 33.9% under Secure Prompt to 91.2% on CyberNative. It also improves CWEval Func-Sec performance from 49.7% to 69.6%. Mechanism-oriented ablations further show that the gains depend on selecting task-relevant risks.