Trishna Chakraborty, Udita Ghosh, Aldair Ernesto Gongora, Ruben Glatt, Yue Dong, Jiachen Li, Amit K. Roy-Chowdhury, Chengyu Song
Laboratories are prone to severe injuries from unsafe actions, yet continuous manual safety monitoring, beyond mandatory pre-lab safety training, is infeasible. Vision-language models (VLMs) offer promise for autonomous laboratory safety monitoring, but their effectiveness in realistic settings is unclear due to the lack of visual evaluation data. Safety incidents are mostly documented as text, and existing studies are largely limited to textual understanding. Since hazardous scenes are unsafe to stage or capture, synthetic visual data becomes the only practical way to study this problem. This motivates us to introduce a structured data generation pipeline that converts textual laboratory scenarios into aligned quadruples using LLM as scene graph architect and image generation model as renderer, yielding \dataset: a synthetic dataset of 1,207 $\langle$image, scene graph, ground truth, hazard relevance mask$\rangle$ quadruples across 362 unique scenarios. Experiments on \dataset and seven open- and closed-source models show that VLMs perform effectively given a textual scene graph but degrade substantially in visual-only settings (i.e., where no scene graph is involved), indicating difficulty in extracting structured object relationships directly from pixels. We thus propose \saga, a post-training framework that leverages reinforcement learning to first structure the visual scene as a scene graph, and then predict hazards while aligning attention with relevant image regions. Evaluations with Qwen 3.5 and Gemma 4 show that our method nearly doubles the $F_1$ score over the baseline on our synthetic dataset and achieves an improvement of $\sim 3\times$ on a mini real-world dataset, while preserving general visual understanding in visual question answering.
Haochen Zeng, Andrew Bao, Jiajun Cheng, Chengyu Song
Proof-of-Vulnerability (PoV) input generation is a critical task in software security and supports downstream applications such as bug reproduction and patch validation. Generating a PoV input requires solving two sets of constraints: (1) reachability constraints for reaching vulnerable code locations, and (2) triggering constraints for activating the target vulnerability. Existing approaches, including directed greybox fuzzing and LLM-assisted fuzzing, struggle to efficiently satisfy these constraints. This work presents an agentic method that mimics human experts. Human analysts iteratively study code to extract semantic reachability and triggering constraints, form hypotheses about PoV triggering strategies, encode them as test inputs, and refine their understanding using debugging feedback. We automate this process with an agentic directed fuzzing framework called PBFuzz. PBFuzz tackles four challenges in agentic PoV generation: autonomous code reasoning for semantic constraint extraction, custom program-analysis tools for targeted inference, persistent memory to avoid hypothesis drift, and property-based testing for efficient constraint solving while preserving input structure. Experiments on the Magma benchmark show strong results. PBFuzz triggered 57 vulnerabilities, surpassing all baselines, and uniquely triggered 17 vulnerabilities not exposed by existing fuzzers. PBFuzz achieved this within a 30-minute budget per target, whereas conventional approaches were allotted 24 hours. Median time-to-exposure was 339 seconds for PBFuzz versus 8,680 seconds for AFL++ with CmpLog, giving a 25.6x efficiency improvement with an API cost of 1.83 USD per vulnerability. In real-world applications, PBFuzz reproduced three FFmpeg 1-day CVEs that had no public PoVs and identified three previously unknown Bento4 vulnerabilities.