Neuroforger generates certified violation witnesses for smart contracts by representing specs as Solidity tests with abstract-type variables, using LLMs to instantiate them, and validating via type checking plus execution.
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LLM-based security code review is vulnerable to framing bias, with a novel iterative refinement attack achieving 100% success in reintroducing vulnerabilities across real projects.
AutoSOUP automates component-level memory-safety verification by generating Safety-Oriented Unit Proofs via three techniques and a hybrid LLM-plus-program-synthesis architecture called LLM-As-Function-Call.
PRAXIS combines LLM-driven structured traversal of service dependency graphs and hammock-block program dependence graphs to improve root-cause analysis accuracy by up to 6.3x while cutting token consumption by 5.3x on 30 real-world cloud incidents.
Introduces a taxonomy of nine LLM code smells, a static detection tool, and reports 73.5% prevalence with 91.3% precision and 71.8% recall across 692 projects.
Agent Mentor analyzes semantic trajectories in agent logs to identify undesired behaviors and derives corrective prompt instructions, yielding measurable accuracy gains on benchmark tasks across three agent setups.
This survey compiles the history, awards, funding, AI integrations, and open challenges of the ESBMC model checker from 2009 to 2026.
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Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
LLM-based security code review is vulnerable to framing bias, with a novel iterative refinement attack achieving 100% success in reintroducing vulnerabilities across real projects.