Asm2Seq: Explainable Assembly Code Functional Summary Generation for Reverse Engineering and Vulnerability Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reverse engineering is the process of understanding the inner working of a software system without having the source code. It is critical for firmware security validation, software vulnerability research, and malware analysis. However, it often requires a significant amount of manual effort. Recently, data-driven solutions were proposed to reduce manual effort by identifying the code clones on the assembly or the source level. However, security analysts still have to understand the matched assembly or source code to develop an understanding of the functionality, and it is assumed that such a matched candidate always exists. This research bridges the gap by introducing the problem of assembly code summarization. Given the assembly code as input, we propose a machine-learning-based system that can produce human-readable summarizations of the functionalities in the context of code vulnerability analysis. We generate the first assembly code to function summary dataset and propose to leverage the encoder-decoder architecture. With the attention mechanism, it is possible to understand what aspects of the assembly code had the largest impact on generating the summary. Our experiment shows that the proposed solution achieves high accuracy and the Bilingual Evaluation Understudy (BLEU) score. Finally, we have performed case studies on real-life CVE vulnerability cases to better understand the proposed method’s performance and practical implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it