FORGE uses a reasoning-action-observation loop and Dynamic Forest of Agents to perform scalable LLM-based binary analysis, finding 1,274 vulnerabilities across 591 of 3,457 real-world firmware binaries at 72.3% precision and broader coverage than prior methods.
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LLM prompting achieves 100% grammar adaptation consistency on small test DSLs and reuses adaptations across QVTo evolution steps, outperforming rule-based methods, but drops below 90% on large grammars like EAST-ADL.
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Feedback-Driven Execution for LLM-Based Binary Analysis
FORGE uses a reasoning-action-observation loop and Dynamic Forest of Agents to perform scalable LLM-based binary analysis, finding 1,274 vulnerabilities across 591 of 3,457 real-world firmware binaries at 72.3% precision and broader coverage than prior methods.
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Leveraging LLMs for Grammar Adaptation: A Study on Metamodel-Grammar Co-Evolution
LLM prompting achieves 100% grammar adaptation consistency on small test DSLs and reuses adaptations across QVTo evolution steps, outperforming rule-based methods, but drops below 90% on large grammars like EAST-ADL.