LOHRE: Execution-Grounded Type Recovery via LLM-Orchestrated Hybrid Analysis
Story 17, from arXiv
LOHRE is a new framework that improves UDT recovery by combining LLM-based hypothesis generation with execution validation and cross-function evidence.
Key points
- LOHRE addresses limitations of existing LLM methods by adding execution-based validation.
- It uses a hypothesize-validate-refine loop with dynamic slice emulation.
- The Execution-Depth Heuristic (EDH) compresses execution traces to reduce costs.
- A cross-function knowledge base aggregates UDT hypotheses and confidence scores.
- LOHRE outperforms static baselines on RealType and Windows RPC datasets.
Why it matters
Software engineers working on reverse engineering or binary analysis may benefit from LOHRE's more accurate and efficient UDT recovery, especially when dealing with stripped binaries or complex type layouts.
Try this
Experiment with dynamic slice emulation and EDH compression in your reverse engineering workflows to improve UDT recovery accuracy.
Counterpoint
LOHRE's effectiveness depends on dynamic execution and may not be feasible for environments where runtime analysis is restricted or impractical.
Sources
This summary was written by a language model and may contain errors. Read the original for the full story.