A developer-centric perspective is adopted and three representative risk scenarios that commonly lead to security vulnerabilities in LLM-generated code are identified: Ambiguous Requirements, Under-Specified Operational Context, and Security--Functionality Conflict are identified.
Abstract
Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored. Existing benchmarks often rely on explicitly specified security requirements, failing to capture real-world scenarios where prompts are frequently ambiguous or incomplete. In this paper, we adopt a developer-centric perspective and identify three representative risk scenarios that commonly lead to security vulnerabilities in LLM-generated code: Ambiguous Requirements, Under-Specified Operational Context, and Security--Functionality Conflict. Based on these scenarios, we construct a large-scale benchmark comprising 2,700 test cases, enabling fine-grained evaluation of LLM security under realistic conditions. Extensive evaluation of eight state-of-the-art LLMs reveals that all models exhibit average vulnerability rates exceeding 56\% across risk scenarios. We further demonstrate that security-aware prompting can substantially mitigate these risks, achieving up to 45\% improvement.
An empirical benchmark evaluating whether LLMs and SLMs can generate security-compliant AWS Terraform configurations suggests that prompt design is a critical factor, highlighting the need for a proper pipeline for developing and validating LLM-assisted secure IaC generation.
Francis Luis Santos Vargas, R. Mansilha, Diego Kreutz· Anais do I Simpósio de Infra...· 0 citations
This work systematically generates syntactic variants of security-relevant code generation prompts and evaluates their impact on code security across multiple open LLMs and programming languages, identifying prompt syntax as a concrete security control surface and providing actionable guidance for reducing vulnerability risk in LLM-assisted development.
Matteo Cicalese, Antonio Della Porta, Stefano Lambiase et al.· arXiv.org· 0 citations
GenIaC-SecBench is introduced, a benchmark of 100 deployment scenarios stratified by architectural complexity, evaluated across 12 model configurations from four vendors, producing 1,196 IaC artifacts scanned by three independent policy engines (Checkov, Trivy, KICS).
RTL-Obliger is presented, a neuro-symbolic framework that infers implicit security obligations of register-transfer-level RTL in a functionality-preserving two-stage generation and raises mean all-pass rates.
Guang Yang, Xing Hu, Xiang Chen et al.· 0 citations
SNIPTEST is an execution-based warning triage framework that generates and fuzzes compiled code slices centered around static-analysis warnings that employs a layer-by-layer slicing strategy, incrementally expanding context around the target location to validate potential vulnerabilities with increasing precision.
Aniruddhan Murali, Noble Saji Mathews, Mahmoud Alfadel et al.· IEEE Transactions on Softwar...· 0 citations
: Large language models are increasingly being deployed in safety-critical domains, yet remain vulnerable to jailbreak attacks that circumvent safety alignments. This systematic review synthesizes empirical jailbreak research published between 2024 and 2025, using a PRISMA-guided search protocol, followed by BERTopic-based topic modeling. The analysis identifies eight main jailbreak categories: optimization-based, ge-netic/evolutionary, iterative refinement, semantic/persuasion-based, decomposition, context/generation-level, visual/encoding and fuzzing attacks, and characterizes their effectiveness, efficiency, and transferability across open-source and proprietary models, including Llama-2/3, Vicuna, GPT-3.5/4, Claude, Gemini, and DeepSeek-V3. Results show that simple configuration and context-level attacks can match the near-perfect attack success rates of sophisticated white-box optimization methods on models such as Llama-2, while requiring far fewer queries and no parameter access, highlighting a gap between research focus and practical threat severity. The review further identifies five recurring vulnerability mechanisms: representation-level gaps, execution-priority manipulation, semantic fragmentation, gradient-space exploitation and persuasion susceptibility, and documents family-specific vulnerability patterns, with open-source Llama-based models consistently more exposed than safety-enhanced architectures such as Claude. Diverse methods, uneven focus on models and publication bias limit how broadly results apply. Nonetheless, the review reveals that weaknesses in safety alignment persist across successive LLM generations, urging that effective defenses must address all eight attack categories rather than isolated techniques.
Meda Račaitytė, Hélder Bastos, R. Ribeiro et al.· International Conference on...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
