JIGSAW: Efficient and Scalable Path Constraints Fuzzing

Coverage-guided testing has shown to be an effective way to find bugs. If we model coverage-guided testing as a search problem (i.e., finding inputs that can cover more branches), then its efficiency mainly depends on two factors: (1) the accuracy of the searching algorithm and (2) the number of inputs that can be evaluated per unit time. Therefore, improving the search throughput has shown to be an effective way to improve the performance of coverage-guided testing.In this work, we present a novel design to improve the search throughput: by evaluating newly generated inputs with JIT-compiled path constraints. This approach allows us to significantly improve the single thread throughput as well as scaling to multiple cores. We also developed several optimization techniques to eliminate major bottlenecks during this process. Evaluation of our prototype JIGSAW shows that our approach can achieve three orders of magnitude higher search throughput than existing fuzzers and can scale to multiple cores. We also find that with such high throughput, a simple gradient-guided search heuristic can solve path constraints collected from a large set of real-world programs faster than SMT solvers with much more sophisticated search heuristics. Evaluation of end-to-end coverage-guided testing also shows that our JIGSAW-powered hybrid fuzzer can outperform state-of-the-art testing tools

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers

Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibility of backdoor attacks against malware classifiers, and yet the stealthiness of such attacks is not well understood. In this paper, we focus on Android malware classifiers and investigate backdoor attacks under the clean-label setting (i.e., attackers do not have complete control over the training process or the labeling of poisoned data). Empirically, we show that existing backdoor attacks against malware classifiers are still detectable by recent defenses such as MNTD. To improve stealthiness, we propose a new attack, Jigsaw Puzzle (JP), based on the key observation that malware authors have little to no incentive to protect any other authors' malware but their own. As such, Jigsaw Puzzle learns a trigger to complement the latent patterns of the malware author's samples, and activates the backdoor only when the trigger and the latent pattern are pieced together in a sample. We further focus on realizable triggers in the problem space (e.g., software code) using bytecode gadgets broadly harvested from benign software. Our evaluation confirms that Jigsaw Puzzle is effective as a backdoor, remains stealthy against state-of-the-art defenses, and is a threat in realistic settings that depart from reasoning about feature-space-only attacks. We conclude by exploring promising approaches to improve backdoor defenses

SentiNet: Detecting Physical Attacks Against Deep Learning Systems

SentiNet is a novel detection framework for physical attacks on neural networks, a class of attacks that constrains an adversarial region to a visible portion of an image. Physical attacks have been shown to be robust and flexible techniques suited for deployment in real-world scenarios. Unlike most other adversarial detection works, SentiNet does not require training a model or preknowledge of an attack prior to detection. This attack-agnostic approach is appealing due to the large number of possible mechanisms and vectors of attack an attack-specific defense would have to consider. By leveraging the neural network's susceptibility to attacks and by using techniques from model interpretability and object detection as detection mechanisms, SentiNet turns a weakness of a model into a strength. We demonstrate the effectiveness of SentiNet on three different attacks - i.e., adversarial examples, data poisoning attacks, and trojaned networks - that have large variations in deployment mechanisms, and show that our defense is able to achieve very competitive performance metrics for all three threats, even against strong adaptive adversaries with full knowledge of SentiNet.Comment: 15 page