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Found 14 papers, 4 papers with code
Do Neutral Prompts Produce Insecure Code? FormAI-v2 Dataset: Labelling Vulnerabilities in Code Generated by Large Language Models
This study provides a comparative analysis of state-of-the-art large language models (LLMs), analyzing how likely they generate vulnerabilities when writing simple C programs using a neutral zero-shot prompt.
Tasks People Prompt: A Taxonomy of LLM Downstream Tasks in Software Verification and Falsification Approaches
Prompting has become one of the main approaches to leverage emergent capabilities of Large Language Models [Brown et al. NeurIPS 2020, Wei et al. TMLR 2022, Wei et al. NeurIPS 2022].
NeuroCodeBench: a plain C neural network benchmark for software verification
Safety-critical systems with neural network components require strong guarantees.
SecureFalcon: The Next Cyber Reasoning System for Cyber Security
Software vulnerabilities leading to various detriments such as crashes, data loss, and security breaches, significantly hinder the quality, affecting the market adoption of software applications and systems.
The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification
This paper presents the FormAI dataset, a large collection of 112, 000 AI-generated compilable and independent C programs with vulnerability classification.
Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices
The field of Natural Language Processing (NLP) is currently undergoing a revolutionary transformation driven by the power of pre-trained Large Language Models (LLMs) based on groundbreaking Transformer architectures.
QNNRepair: Quantized Neural Network Repair
It accepts the full-precision and weight-quantized neural networks and a repair dataset of passing and failing tests.
A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification
In this paper we present a novel solution that combines the capabilities of Large Language Models (LLMs) with Formal Verification strategies to verify and automatically repair software vulnerabilities.
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study
However, we need to also consider the potential of attacks targeting the underlying AI systems (e. g., adversaries seek to corrupt data on the IoT devices during local updates or corrupt the model updates); hence, in this article, we propose an anticipatory study for poisoning attacks in federated edge learning for digital twin 6G-enabled IoT environments.
LF-checker: Machine Learning Acceleration of Bounded Model Checking for Concurrency Verification (Competition Contribution)
We describe and evaluate LF-checker, a metaverifier tool based on machine learning.
CEG4N: Counter-Example Guided Neural Network Quantization Refinement
Existing quantization techniques tend to degrade the network accuracy.
Incremental Bounded Model Checking of Artificial Neural Networks in CUDA
Artificial Neural networks (ANNs) are powerful computing systems employed for various applications due to their versatility to generalize and to respond to unexpected inputs/patterns.
Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)
We describe and evaluate a novel optimization-based off-line path planning algorithm for mobile robots based on the Counterexample-Guided Inductive Optimization (CEGIO) technique.
Counterexample Guided Inductive Optimization
This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers.
