RealVuln benchmark finds security-specialized scanners outperform general-purpose LLMs and rule-based SAST tools on hand-labeled vulnerable Python code under F3 scoring, with all artifacts released.
Dubniczky, Krisztofer Zoltán Horvát, Tamás Bisztray, Mohamed Amine Fer- rag, Lucas C
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Training Qwen3-8B on symbolic execution traces from Soteria improves violation detection in C programs by over 17 points, transfers across five property types, and shows superadditive gains with chain-of-thought.
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
citing papers explorer
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RealVuln: Benchmarking Rule-Based, General-Purpose LLM, and Security-Specialized Scanners on Real-World Code
RealVuln benchmark finds security-specialized scanners outperform general-purpose LLMs and rule-based SAST tools on hand-labeled vulnerable Python code under F3 scoring, with all artifacts released.
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Teaching LLMs Program Semantics via Symbolic Execution Traces
Training Qwen3-8B on symbolic execution traces from Soteria improves violation detection in C programs by over 17 points, transfers across five property types, and shows superadditive gains with chain-of-thought.
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From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.