10 citations · 20 across the 5 of their papers we have counts for
5 papers
Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency
Chris Adams, Arjun Singh Banga, Parveen Bansal +28
AI-assisted coding tools have altered software production. At Meta, significant lines of code per human-landed diff grew by 105.9% year over year and per-developer diff volume rose…
CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning
Lauren Deason, Adam Bali, Ciprian Bejean +20
Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhanc…
LlamaFirewall: An open source guardrail system for building secure AI agents
Sahana Chennabasappa, Cyrus Nikolaidis, Daniel Song +16
Large language models (LLMs) have evolved from simple chatbots into autonomous agents capable of performing complex tasks such as editing production code, orchestrating workflows,…
CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models
Shengye Wan, Cyrus Nikolaidis, Daniel Song +10
We are releasing a new suite of security benchmarks for LLMs, CYBERSECEVAL 3, to continue the conversation on empirically measuring LLM cybersecurity risks and capabilities. CYBERS…
CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models
Manish Bhatt, Sahana Chennabasappa, Yue Li +10
Large language models (LLMs) introduce new security risks, but there are few comprehensive evaluation suites to measure and reduce these risks. We present BenchmarkName, a novel be…