RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated Code

Zacharie Chenail-Larcher; Daniele Lunghi; Melissa Tessa; Quentin Stiévenart; Jacques Klein; Tegawendé F. Bissyandé; Florent Avellaneda; Naouel Moha

arXiv · 2610.40735

RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated Code

Zacharie Chenail-Larcher·Daniele Lunghi·Melissa Tessa·Quentin Stiévenart·Jacques Klein·Tegawendé F. Bissyandé·Florent Avellaneda·Naouel Moha

View original source ↗Read source document ↗Export metadata ↓

Abstract

Evaluating approaches that improve the security of LLM code generation requires datasets that jointly assess security and functionality and remain adapted to the capabilities of the models being studied. However, existing benchmarks are costly to produce and are generally not calibrated to a chosen range of model capabilities. We introduce RiskyPy, an automated pipeline that generates security-oriented code-generation datasets calibrated to model proficiency using functionality and vulnerability canaries. We instantiate RiskyPy for Python using domains covering 18 CWEs from the 2025 CWE Top 25, producing 101 prompts. Experiments on Qwen3-4B and Seed-Coder-8B show that the generated tasks remain functionally accessible, with 57-72% of prompts yielding functional code, while only 5-10% of generations are free of Bandit findings, leaving room for joint evaluation of security and functionality of security improvement approaches.

Explore related subjects

cs.CR cs.AI cs.SE

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Zacharie Chenail-Larcher, Daniele Lunghi, Melissa Tessa, Quentin Stiévenart, Jacques Klein, Tegawendé F. Bissyandé, Florent Avellaneda, Naouel Moha. 2026-10-29. RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated Code. https://arxiv.org/abs/2610.40735

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A Survey on Reconfigurable Intelligent Surfaces Towards Practical Deployments: Security and Privacy Perspectives

Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves. Although RISs have been widely studied for communication, sensing, and localization, their security and privacy implications are still fragmented across application-specific studies, prototypes, and testbeds. This survey provides a deployment-oriented review of RIS applications, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use. We analyze scenarios with two types of systems (with and without legitimate RIS) and two types of attackers (with and without malicious RIS), and clarify how RIS introduces new attack surfaces to practical systems, including eavesdropping, jamming, and spoofing attacks. In response, we review defenses against RIS-related attacks in these systems, such as protocol- and algorithm-level protections, unauthorized RIS detection, and interference/disruption-based mitigation. We also discuss scenarios in which the legitimate user applies an additional RIS to defend against attacks. To support future research, we also curate papers and available open resources at https://awesome-ris-security.github.io/. By highlighting RIS's functionality and its security/privacy challenges and opportunities, this survey aims to guide researchers and engineers toward the development of secure, resilient, and privacy-preserving RIS-enabled practical wireless systems and environments.

cs.CR↗

arXiv2026

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak attack, termed CodeSpear, that exploits GCD to induce LLMs into generating malicious code. Our experiments show that simply applying a benign code grammar constraint can effectively jailbreak LLMs. To address this vulnerability, we propose CodeShield, a safety alignment approach that robustly preserves safe behavior even under attacker-controlled grammar constraints. CodeShield aligns the model in the code modality by teaching it to generate honeypot code under GCD. Such code is semantically harmless, so it does not implement the malicious request, and structurally diverse, so it is difficult to suppress through grammar tightening. At the same time, CodeShield still preserves natural-language refusals when natural language is available. Experiments on 10 popular LLMs across 4 benchmarks show that CodeSpear outperforms representative jailbreak baselines and increases the attack success rate by more than 30 percentage points on average. CodeShield also restores safety under CodeSpear while preserving benign utility. Our findings reveal a fundamental risk of GCD and call for greater attention to its potential security implications.

cs.CR↗

arXiv2026

Jaguar: Fast Private Neural Network Inference with Power-of-Two Homomorphic Arithmetic

Hybrid HE/2PC private neural network inference remains bottlenecked by prime-modulus homomorphic arithmetic in convolution and by a precision flow that runs ReLU at doubled bitwidth before invoking a separate truncation protocol. We present Jaguar, a system built on a single design choice--a power-of-two ciphertext ring--that addresses both. The choice enables SPA-Conv, a coefficient-domain convolution kernel that replaces NTT-centric polynomial multiplication with Scalar-Polynomial Accumulation, and an exact ciphertext-side truncation by local right shifts that lets ReLU run directly at the target fixed-point precision and eliminates the post-ReLU truncation protocol. Where NTT remains genuinely useful--at the client, for the single polynomial multiplication during decryption--we recover it through an auxiliary NTT prime, preserving the power-of-two protocol substrate while keeping decryption O(N log N). On ImageNet-scale ResNet-18, ResNet-50, and MobileNetV2 with AVX disabled, Jaguar achieves 2.07-3.72x lower end-to-end latency than Cheetah and 2.16-3.36x lower than Rhombus, with 1.16-1.76x lower communication than Cheetah.

cs.CR↗