6 papers
RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated Code
Zacharie Chenail-Larcher, Daniele Lunghi, Melissa Tessa +5
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 th…
Trajectory-Level Security Debt in LLM Coding Agents
Prateek Kumar Rajput, Abdoul Kader Kabore, Yewei Song +4
LLM coding agents can traverse hundreds of intermediate code states before submitting a solution. Evaluating only the final artifact leaves the evolution of security findings unmea…
Evaluating Inference-Time Defenses Against Package Hallucination in LLM-Generated Code
Alberick Euraste Djire, Iyiola E. Olatunji, Melissa Tessa +3
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We…
Adversarial Camouflage
Paweł Borsukiewicz, Daniele Lunghi, Melissa Tessa +2
While the rapid development of facial recognition algorithms has enabled numerous beneficial applications, their widespread deployment has raised significant concerns about the ris…
How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test
Melissa Tessa, Iyiola E. Olatunji, Aicha War +2
Recent secure code generation methods, using vulnerability-aware fine-tuning, prefix-tuning, and prompt optimization, claim to prevent LLMs from producing insecure code. However, t…
A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models
Melissa Tessa, Diderot D. Cidjeu, Rachele Carli +4
Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more ela…