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Found 6 papers, 3 papers with code
CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models
We present BenchmarkName, a novel benchmark to quantify LLM security risks and capabilities.
Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models
This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants.
A Language-Agnostic Model for Semantic Source Code Labeling
We use Stack Overflow code snippets and their tags to train a language-agnostic, deep convolutional neural network to automatically predict semantic labels for source code documents.
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content
Malicious web content is a serious problem on the Internet today.
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys
For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack.
Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features
Further, we confirm our false positive rates directly on a live stream of files coming in from Invincea's deployed endpoint solution, provide an estimate of how many new binary files we expected to see a day on an enterprise network, and describe how that relates to the false positive rate and translates into an intuitive threat score.
Cryptography and Security
