Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies

• URL: http://arxiv.org/abs/2510.22944v1
• Date: Mon, 27 Oct 2025 02:59:17 GMT
• Title: Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies
• Authors: Bin Wang, YiLu Zhong, MiDi Wan, WenJie Yu, YuanBing Ouyang, Yenan Huang, Hui Li,
• Abstract summary: Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical concern.<n>We propose an evaluation framework for prompt quality encompassing three key dimensions: goal clarity, information completeness, and logical consistency.<n>Our findings highlight that enhancing the quality of user prompts constitutes a critical and effective strategy for strengthening the security of AI-generated code.
• Score: 4.435429537888066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical concern. Existing studies predominantly concentrate on adversarial attacks or inherent flaws within the models. However, a more prevalent yet underexplored issue concerns how the quality of a benign but poorly formulated prompt affects the security of the generated code. To investigate this, we first propose an evaluation framework for prompt quality encompassing three key dimensions: goal clarity, information completeness, and logical consistency. Based on this framework, we construct and publicly release CWE-BENCH-PYTHON, a large-scale benchmark dataset containing tasks with prompts categorized into four distinct levels of normativity (L0-L3). Extensive experiments on multiple state-of-the-art LLMs reveal a clear correlation: as prompt normativity decreases, the likelihood of generating insecure code consistently and markedly increases. Furthermore, we demonstrate that advanced prompting techniques, such as Chain-of-Thought and Self-Correction, effectively mitigate the security risks introduced by low-quality prompts, substantially improving code safety. Our findings highlight that enhancing the quality of user prompts constitutes a critical and effective strategy for strengthening the security of AI-generated code.

Related papers

• Inference-Time Safety For Code LLMs Via Retrieval-Augmented Revision [3.983997834693767]
  Large Language Models (LLMs) are increasingly deployed for code generation in high-stakes software development.<n>LLMs cannot readily adapt to newly discovered vulnerabilities or changing security standards without retraining.<n>We present a principled approach to trustworthy code generation by design that operates as an inference-time safety mechanism.
  arXiv  Detail & Related papers  (2026-03-02T06:06:34Z)
• The Shadow Self: Intrinsic Value Misalignment in Large Language Model Agents [37.75212140218036]
  We formalize the Loss-of-Control risk and identify the previously underexamined Intrinsic Value Misalignment (Intrinsic VM)<n>We then introduce IMPRESS, a scenario-driven framework for systematically assessing this risk.<n>We evaluate Intrinsic VM on 21 state-of-the-art LLM agents and find that it is a common and broadly observed safety risk across models.
  arXiv  Detail & Related papers  (2026-01-24T07:09:50Z)
• STaR: Sensitive Trajectory Regulation for Unlearning in Large Reasoning Models [12.133996629992318]
  We propose a parameter-free, inference-time unlearning framework that achieves robust privacy protection throughout the reasoning process.<n>Experiments on the R-TOFU benchmark demonstrate that STaR achieves comprehensive and stable unlearning with minimal utility loss.
  arXiv  Detail & Related papers  (2026-01-14T08:35:23Z)
• Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety [59.01189713115365]
  We evaluate the impact of explicitly specifying extensive safety codes versus demonstrating them through illustrative cases.<n>We find that referencing explicit codes inconsistently improves harmlessness and systematically degrades helpfulness.<n>We propose CADA, a case-augmented deliberative alignment method for LLMs utilizing reinforcement learning on self-generated safety reasoning chains.
  arXiv  Detail & Related papers  (2026-01-12T21:08:46Z)
• CARE: Decoding Time Safety Alignment via Rollback and Introspection Intervention [68.95008546581339]
  Existing decoding-time interventions, such as Contrastive Decoding, often force a severe trade-off between safety and response quality.<n>We propose CARE, a novel framework for decoding-time safety alignment that integrates three key components.<n>The framework achieves a superior balance of safety, quality, and efficiency, attaining a low harmful response rate and minimal disruption to the user experience.
  arXiv  Detail & Related papers  (2025-09-01T04:50:02Z)
• SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code [7.209766132478914]
  We introduce SafeGenBench, a benchmark specifically designed to assess the security of LLM-generated code.<n>The dataset encompasses a wide range of common software development scenarios and vulnerability types.<n>Through the empirical evaluation of state-of-the-art LLMs on SafeGenBench, we reveal notable deficiencies in their ability to produce vulnerability-free code.
  arXiv  Detail & Related papers  (2025-06-06T02:48:02Z)
• Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning [53.92712851223158]
  We formulate safety and privacy issues into contextualized compliance problems following the Contextual Integrity (CI) theory.<n>Under the CI framework, we align our model with three critical regulatory standards: EU AI Act, and HIPAA.<n>We employ reinforcement learning (RL) with a rule-based reward to incentivize contextual reasoning capabilities while enhancing compliance with safety and privacy norms.
  arXiv  Detail & Related papers  (2025-05-20T16:40:09Z)
• Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs [83.11815479874447]
  We propose a novel jailbreak attack framework, inspired by cognitive decomposition and biases in human cognition.<n>We employ cognitive decomposition to reduce the complexity of malicious prompts and relevance bias to reorganize prompts.<n>We also introduce a ranking-based harmfulness evaluation metric that surpasses the traditional binary success-or-failure paradigm.
  arXiv  Detail & Related papers  (2025-05-03T05:28:11Z)
• CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation [20.72188827088484]
  Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing.<n> detecting vulnerabilities in functionally correct code is more challenging, especially for developers with limited security knowledge.<n>We introduce CWEval, a novel outcome-driven evaluation framework designed to enhance the evaluation of secure code generation by LLMs.
  arXiv  Detail & Related papers  (2025-01-14T15:27:01Z)
• SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI [58.29510889419971]
  Existing benchmarks for evaluating the security risks and capabilities of code-generating large language models (LLMs) face several key limitations.<n>We introduce a general and scalable benchmark construction framework that begins with manually validated, high-quality seed examples and expands them via targeted mutations.<n>Applying this framework to Python, C/C++, and Java, we build SeCodePLT, a dataset of more than 5.9k samples spanning 44 CWE-based risk categories and three security capabilities.
  arXiv  Detail & Related papers  (2024-10-14T21:17:22Z)
• Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level [10.476222570886483]
  Large language models (LLMs) have demonstrated immense utility across various industries.<n>As LLMs advance, the risk of harmful outputs increases due to incorrect or malicious instruction prompts.<n>This paper examines the LLMs' capability to recognize harmful outputs, revealing and quantifying their proficiency in assessing the danger of previous tokens.
  arXiv  Detail & Related papers  (2024-10-09T12:09:30Z)
• CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
  This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs. Our studies reveal a new and universal safety vulnerability of these models against code input. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
  arXiv  Detail & Related papers  (2024-03-12T17:55:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.