Code-view ensemble lifts LLM vulnerability detection to 78% F1
Source, AST, and CFG models are complementary; logical rules plus majority voting beat the best single view by 5 F1 points.
Stories per day · 30 days
4 stories, newest first · one card per story
Stories tagged #control-flow-graph
Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection
Message-passing choice decides whether Windows malware-detection graph networks survive a later-year sample shift.
SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code
SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.
No Bit Left Behind: Using Brute-Force Lifting to Achieve Fully Static Binary Recompilation
Prototype binary lifter brute-force lifts every byte offset of x86-64 binaries to LLVM IR, enabling fully static cross-ISA recompilation without runtime support.