Labrador: Response Guided Directed Fuzzing for Black-box IoT Devices
Bibliographic record
Abstract
Fuzzing is a popular solution to finding vulnerabilities in software including IoT firmware. However, due to the challenges of emulating or rehosting firmware, some IoT devices (e.g., enterprise-level devices) can only be fuzzed in a black-box manner, which makes fuzzers blind and inefficient due to missing feedbacks (e.g., code coverage or distance). In this paper, we present a novel response guided directed fuzzing solution Labrador, able to test black-box IoT devices efficiently. Specifically, we leverage the network response to infer the execution trace of firmware and deduce the code coverage of testing. Second, we leverage the test case (i.e., request) and its response to estimate the distance to the target sensitive code (i.e., sink). Lastly, we further leverage the distance to guide test case mutation, which efficiently drives directed fuzzing toward candidate vulnerable code. We have implemented a prototype of Labrador and evaluated it on 14 different enterprise-level IoT devices. Results showed that Labrador significantly outperforms state-of-the-art (SOTA) solutions. It finds 44X more vulnerabilities than SNIPUZZ, BOOFUZZ and FIRM-AFL and 8.57X more vulnerabilities than SaTC. In total, it discovered 79 unknown vulnerabilities, of which 61 were assigned with CVEs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".