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Found 16 papers, 8 papers with code
EnIGMA: Enhanced Interactive Generative Model Agent for CTF Challenges
Although language model (LM) agents are demonstrating growing potential in many domains, their success in cybersecurity has been limited due to simplistic design and the lack of fundamental features for this domain.
ARVO: Atlas of Reproducible Vulnerabilities for Open Source Software
High-quality datasets of real-world vulnerabilities are enormously valuable for downstream research in software security, but existing datasets are typically small, require extensive manual effort to update, and are missing crucial features that such research needs.
NYU CTF Dataset: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
Large Language Models (LLMs) are being deployed across various domains today.
Model Cascading for Code: Reducing Inference Costs with Model Cascading for LLM Based Code Generation
Meanwhile, model cascading has been proven effective to conserve computational resources while enhancing accuracy in LLMs on natural language generation tasks.
VeriGen: A Large Language Model for Verilog Code Generation
In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and modeling digital systems.
(Security) Assertions by Large Language Models
As vulnerabilities in hardware can have severe implications on a system, there is a need for techniques to support security verification activities.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #50 on
Code Generation
on MBPP
Can deepfakes be created by novice users?
We conclude that creating Deepfakes is a simple enough task for a novice user given adequate tools and time; however, the resulting Deepfakes are not sufficiently real-looking and are unable to completely fool detection software as well as human examiners
Beyond the C: Retargetable Decompilation using Neural Machine Translation
We explore a different tradeoff that, to the extent possible, treats the assembly and source languages as plain text, and show that this allows us to build a decompiler that is easily retargetable to new languages.
Benchmarking Large Language Models for Automated Verilog RTL Code Generation
Automating hardware design could obviate a significant amount of human error from the engineering process and lead to fewer errors.
Pop Quiz! Can a Large Language Model Help With Reverse Engineering?
Large language models (such as OpenAI's Codex) have demonstrated impressive zero-shot multi-task capabilities in the software domain, including code explanation.
Examining Zero-Shot Vulnerability Repair with Large Language Models
We perform a large scale study of five commercially available, black-box, "off-the-shelf" LLMs, as well as an open-source model and our own locally-trained model, on a mix of synthetic, hand-crafted, and real-world security bug scenarios.
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
The most notable of these comes in the form of the first self-described `AI pair programmer', GitHub Copilot, a language model trained over open-source GitHub code.
NNoculation: Catching BadNets in the Wild
This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, repairs a BadNet both pre-deployment and online in response to backdoored test inputs encountered in the field.
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Our work provides the first step toward defenses against backdoor attacks in deep neural networks.
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
These results demonstrate that backdoors in neural networks are both powerful and---because the behavior of neural networks is difficult to explicate---stealthy.
