Scaling Attacks on Large Logic-Locked Designs
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
Researchers have developed numerous strategies to alleviate the threat of malicious third-party foundries, including logic locking and its numerous sophisticated variants for hardware intellectual property (IP) protection. Recent work at the register-transfer level has opened the door to “large-scale” locking of large IPs (comprising thousands of gates) with hundreds to thousands of key bits. Recent security evaluation of such techniques treats the locked design as a monolith and has suggested that large logic-locked designs are practically secure, even from powerful SAT-based attacks. In this work, we challenge such findings by proposing and evaluating a novel algorithmic method to de-obfuscate large logic-locked circuits by attacking a set of small sub-circuit cones. The algorithm chooses a sub-optimal set of sub-circuit cones and proposes an attack sequence on these cones by leveraging the observation that each locking key-bit is distributed across multiple sub-circuit cones of varying sizes. This Divide And Conquer SAT (DACSAT) attack framework can de-obfuscate large designs, like an AES IP comprising 300,000 gates, logic-locked with up to 50,000 keys in around 3600 seconds, while an out-of-the-box, state-of-the-art SAT attack tool fails.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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