Malware detection and classification has been the focus of extensive research over many years. However, less effort has been devoted to developing post-detection systems that identify specific malicious capabilities (or behaviors) in malware. Such systems play a critical part in identifying and mitigating the damage caused by malware attacks. Unfortunately, current methods for identifying malware capabilities involve substantial manual reverse engineering efforts and context switching between multiple tools, which slows down an investigation and gives attackers an advantage.In this paper, we propose DEEPCAPA, an automated post-detection system that uses machine learning to identify potentially malicious capabilities in malware in the form of MITRE ATT&CK techniques. Our system operates on sequences of API calls, extracted from the memory snapshots taken at key points during the (sandboxed) execution of malware. Our results demonstrate that DEEPCAPA can accurately identify malicious capabilities, achieving a precision of 95.80% and a recall of 93.76% across 29 different techniques.

Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. They also prevent people from misusing images, especially those generated by AI models. We propose a family of regeneration attacks to remove these invisible watermarks. The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image. This approach is flexible and can be instantiated with many existing image-denoising algorithms and pre-trained generative models such as diffusion models. Through formal proofs and extensive empirical evaluations, we demonstrate that pixel-level invisible watermarks are vulnerable to this regeneration attack. Our results reveal that, across four different pixel-level watermarking schemes, the proposed method consistently achieves superior performance compared to existing attack techniques, with lower detection rates and higher image quality. However, watermarks that keep the image semantically similar can be an alternative defense against our attacks. Our finding underscores the need for a shift in research/industry emphasis from invisible watermarks to semantic-preserving watermarks. Code is available at https://github.com/XuandongZhao/WatermarkAttacker

With the continuous rise in the popularity of Android mobile devices, automated testing of apps has become more important than ever. Android apps are event-driven programs. Unfortunately, generating all possible types of events by interacting with an app's interface is challenging for an automated testing approach. Callback-driven testing eliminates the need for event generation by directly invoking app callbacks. However, existing callback-driven testing techniques assume prior knowledge of Android callbacks, and they rely on a human expert, who is familiar with the Android API, to write stub code that prepares callback arguments before invocation. Since the Android API is very large and keeps evolving, prior techniques could only support a small fraction of callbacks present in the Android framework. In this work, we introduce Columbus, a callback-driven testing technique that employs two strategies to eliminate the need for human involvement: (i) it automatically identifies callbacks by simultaneously analyzing both the Android framework and the app under test; (ii) it uses a combination of under-constrained symbolic execution (primitive arguments), and type-guided dynamic heap introspection (object arguments) to generate valid and effective inputs. Lastly, Columbus integrates two novel feedback mechanisms-data dependency and crash-guidance- during testing to increase the likelihood of triggering crashes and maximizing coverage. In our evaluation, Columbus outperforms state-of-the-art model-driven, checkpoint-based, and callback-driven testing tools both in terms of crashes and coverage.