Real-Time Detection of Bitstream Vulnerabilities in FPGAs
Bibliographic record
Abstract
Field-Programmable Gate Arrays (FPGAs) are increasingly used in critical applications and as versatile platforms for research, prototyping, and education. Their reprogrammable nature, however, makes them vulnerable to security threats, particularly through bitstream vulnerabilities. This paper presents a new approach for the real-time detection and mitigation of such vulnerabilities. We introduce BitVulLLM, a fine-tuned variant of LLAMA2 specifically used for FPGA bitstream vulnerability detection. As part of this research, we generated a comprehensive dataset of FPGA bitstreams, including secure and vulnerable configurations, to address the challenges of detecting and rectifying security flaws. Our method not only identifies vulnerabilities with high precision, but also generates secure bitstreams, significantly bolstering the security of embedded systems. This research represents a significant advancement in safeguarding FPGA applications and other use cases from cyber threats, improving the overall performance and reliability of embedded systems and hardware security.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".