The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs

• URL: http://arxiv.org/abs/2607.15937v1
• Date: Fri, 17 Jul 2026 13:20:21 GMT
• Title: The Language of Security: How Prompt Syntax Shapes Secure Code Generation in Open LLMs
• Abstract summary: Large Language Models (LLMs) are increasingly used for source code generation despite their outputs often exhibiting security vulnerabilities.<n>We show that specific syntactic elements, such as constraints, guards, and concept bindings, and their position within the prompt consistently affect the likelihood of generating insecure code.<n>These findings identify prompt syntax as a concrete security control surface and provide actionable guidance for reducing vulnerability risk in LLM-assisted development.
• Score: 12.213106649470973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are increasingly used for source code generation despite their outputs often exhibiting security vulnerabilities. Prior work shows that prompt engineering can mitigate such risks, yet (1) they focused on high-level prompting strategies, neglecting recent evidence that fine-grained syntactic variations can substantially alter model behavior; and (2) predominantly evaluate proprietary LLMs, limiting the applicability of their findings in industrial settings where self-hosted, open models are preferred for privacy, compliance, and deployment control. In this paper, we study how fine-grained syntactic constituents of prompts influence the security of open LLM-generated code. Using a parser-driven approach, we systematically generate syntactic variants of security-relevant code generation prompts and evaluate their impact on code security across multiple open LLMs and programming languages. Our results show that specific syntactic elements, such as constraints, guards, conditions, and concept bindings, and their position within the prompt consistently affect the likelihood of generating insecure code. These findings identify prompt syntax as a concrete security control surface and provide actionable guidance for reducing vulnerability risk in LLM-assisted development.

Related papers

• Minimal Prompt Perturbations Lead to Code Vulnerabilities: Prompt Fragility and Hidden-State Signals in Coding LLMs [46.747768845221735]
  We show that mutations as small as a single-character change can flip generated code from secure to vulnerable.<n>Results show that input-handling flaws can be caught before generation while secure-defaults flaws require intervention during decoding.
  arXiv  Detail & Related papers  (2026-05-28T10:30:28Z)
• An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods [11.454777386934632]
  Large Language Models (LLMs) for automated code generation has enhanced software development efficiency, but often at the cost of security.<n>We present a comprehensive empirical evaluation of the security quality of LLM-generated code across five LLMs and four programming languages.<n>We introduce a weaknesses-aware zero-shot chain-of-thought (WA-0CoT) prompting strategy that enriches prompts with security context using CWE mappings to guide model reasoning.
  arXiv  Detail & Related papers  (2026-05-22T23:52:33Z)
• TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code [46.747768845221735]
  Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages.<n>Their outputs often contain subtle but critical vulnerabilities, posing significant risks when deployed in security-sensitive or mission-critical systems.<n>This paper introduces TypePilot, an agentic AI framework designed to enhance the security and robustness of LLM-generated code.
  arXiv  Detail & Related papers  (2025-10-13T08:44:01Z)
• How Good LLM-Generated Password Policies Are? [0.1747820331822631]
  We study the application of Large Language Models within the context of Cybersecurity Access Control Systems.<n>Specifically, we investigate the consistency and accuracy of LLM-generated password policies, translating natural language prompts into executable pwquality.conf configuration files.<n>Our findings underscore significant challenges in the current generation of LLMs and contribute valuable insights into refining the deployment of LLMs in Access Control Systems.
  arXiv  Detail & Related papers  (2025-06-10T01:12:31Z)
• Smoke and Mirrors: Jailbreaking LLM-based Code Generation via Implicit Malicious Prompts [5.718926328180089]
  This paper introduces a jailbreaking approach, CodeJailbreaker, designed to uncover safety concerns in code generation.<n> Experiments on the recently-released RMCBench benchmark demonstrate that CodeJailbreaker markedly surpasses the conventional jailbreaking strategy.
  arXiv  Detail & Related papers  (2025-03-23T06:06:12Z)
• Compromising Embodied Agents with Contextual Backdoor Attacks [69.71630408822767]
  Large language models (LLMs) have transformed the development of embodied intelligence. This paper uncovers a significant backdoor security threat within this process. By poisoning just a few contextual demonstrations, attackers can covertly compromise the contextual environment of a black-box LLM.
  arXiv  Detail & Related papers  (2024-08-06T01:20:12Z)
• Can We Trust Large Language Models Generated Code? A Framework for In-Context Learning, Security Patterns, and Code Evaluations Across Diverse LLMs [2.7138982369416866]
  Large Language Models (LLMs) have revolutionized automated code generation in software engineering. However, concerns have arisen regarding the security and quality of the generated code. Our research aims to tackle these issues by introducing a framework for secure behavioral learning of LLMs.
  arXiv  Detail & Related papers  (2024-06-18T11:29:34Z)
• 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)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Red Teaming Language Model Detectors with Language Models [114.36392560711022]
  Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. Recent works have proposed algorithms to detect LLM-generated text and protect LLMs. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
  arXiv  Detail & Related papers  (2023-05-31T10:08:37Z)

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.