QueryX: Symbolic Query on Decompiled Code for Finding Bugs in COTS Binaries
Bibliographic record
Abstract
Extensible static checking tools, such as Sys and CodeQL, have successfully discovered bugs in source code. These tools allow analysts to write application-specific rules, referred to as queries. These queries can leverage the domain knowledge of analysts, thereby making the analysis more accurate and scalable. However, the majority of these tools are inapplicable to binary-only analysis. One exception, joern, translates a binary code into decompiled code and feeds the decompiled code into an ordinary C code analyzer. However, this approach is not sufficiently precise for symbolic analysis, as it overlooks the unique characteristics of decompiled code. While binary analysis platforms, such as angr, support symbolic analysis, analysts must understand their intermediate representations (IRs) although they are mostly working with decompiled code.In this paper, we propose a precise and scalable symbolic analysis called fearless symbolic analysis that uses intuitive queries for binary code and implement this in QueryX. To make the query intuitive, QueryX enables analysts to write queries on top of decompiled code instead of IRs. In particular, QueryX supports callbacks on decompiled code, using which analysts can control symbolic analysis to discover bugs in the code. For precise analysis, we lift decompiled code into our IR named DNR and perform symbolic analysis on DNR while considering the characteristics of the decompiled code. Notably, DNR is only used internally such that it allows analysts to write queries regardless of using DNR. For scalability, QueryX automatically reduces control-flow graphs using callbacks and ordering dependencies between callbacks that are specified in the queries. We applied QueryX to the Windows kernel, the Windows system service, and an automotive binary. As a result, we found 15 unique bugs including 10 CVEs and earned $180,000 from the Microsoft bug bounty program.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".