OSPREY: Recovery of Variable and Data Structure via Probabilistic Analysis for Stripped Binary
Bibliographic record
Abstract
Recovering variables and data structure information from stripped binary is a prominent challenge in binary program analysis. While various state-of-the-art techniques are effective in specific settings, such effectiveness may not generalize. This is mainly because the problem is inherently uncertain due to the information loss in compilation. Most existing techniques are deterministic and lack a systematic way of handling such uncertainty. We propose a novel probabilistic technique for variable and structure recovery. Random variables are introduced to denote the likelihood of an abstract memory location having various types and structural properties such as being a field of some data structure. These random variables are connected through probabilistic constraints derived through program analysis. Solving these constraints produces the posterior probabilities of the random variables, which essentially denote the recovery results. Our experiments show that our technique substantially outperforms a number of state-of-the-art systems, including IDA, Ghidra, Angr, and Howard. Our case studies demonstrate the recovered information improves binary code hardening and binary decompilation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".