Hardware-Assisted Circumvention of Self-Hashing Software Tamper Resistance
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
Self-hashing has been proposed as a technique for verifying software integrity. Appealing aspects of this approach to software tamper resistance include the promise of being able to verify the integrity of software independent of the external support environment, as well as the ability to integrate code protection mechanisms automatically. In this paper, we show that the rich functionality of most modern general-purpose processors (including UltraSparc, x86, PowerPC, AMD64, Alpha, and ARM) facilitate an automated, generic attack which defeats such self-hashing. We present a general description of the attack strategy and multiple attack implementations that exploit different processor features. Each of these implementations is generic in that it can defeat self-hashing employed by any user-space program on a single platform. Together, these implementations defeat self-hashing on most modern general-purpose processors. The generality and efficiency of our attack suggests that self-hashing is not a viable strategy for high-security tamper resistance on modern computer systems.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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