Labrador: Response Guided Directed Fuzzing for Black-box IoT Devices
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
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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