Salman Ahmed

Salman Ahmed, Ya Xiao, Taejoong et al.

Google and Apple jointly introduced a digital contact tracing technology and an API called "exposure notification," to help health organizations and governments with contact tracing. The technology and its interplay with security and privacy constraints require investigation. In this study, we examine and analyze the security, privacy, and reliability of the technology with actual and typical scenarios (and expected typical adversary in mind), and quite realistic use cases. We do it in the context of Virginia's COVIDWISE app. This experimental analysis validates the properties of the system under the above conditions, a result that seems crucial for the peace of mind of the exposure notification technology adopting authorities, and may also help with the system's transparency and overall user trust.

Ya Xiao, Salman Ahmed, Wenjia Song et al.

Despite recent research efforts, the vision of automatic code generation through API recommendation has not been realized. Accuracy and expressiveness challenges of API recommendation needs to be systematically addressed. We present a new neural network-based approach, Multi-HyLSTM for API recommendation --targeting cryptography-related code. Multi-HyLSTM leverages program analysis to guide the API embedding and recommendation. By analyzing the data dependence paths of API methods, we train embedding and specialize a multi-path neural network architecture for API recommendation tasks that accurately predict the next API method call. We address two previously unreported programming language-specific challenges, differentiating functionally similar APIs and capturing low-frequency long-range influences. Our results confirm the effectiveness of our design choices, including program-analysis-guided embedding, multi-path code suggestion architecture, and low-frequency long-range-enhanced sequence learning, with high accuracy on top-1 recommendations. We achieve a top-1 accuracy of 91.41% compared with 77.44% from the state-of-the-art tool SLANG. In an analysis of 245 test cases, compared with the commercial tool Codota, we achieve a top-1 recommendation accuracy of 88.98%, which is significantly better than Codota's accuracy of 64.90%. We publish our data and code as a large Java cryptographic code dataset.

Salman Ahmed, Ya Xiao, Gang Tan et al.

Just-in-time return-oriented programming (JIT-ROP) allows one to dynamically discover instruction pages and launch code reuse attacks, effectively bypassing most fine-grained address space layout randomization (ASLR) protection. However, in-depth questions regarding the impact of code (re-)randomization on code reuse attacks have not been studied. For example, how would one compute the re-randomization interval effectively by considering the speed of gadget convergence to defeat JIT-ROP attacks?; how do starting pointers in JIT-ROP impact gadget availability and gadget convergence time?; what impact do fine-grained code randomizations have on the Turing-complete expressive power of JIT-ROP payloads? We conduct a comprehensive measurement study on the effectiveness of fine-grained code randomization schemes, with 5 tools, 20 applications including 6 browsers, 1 browser engine, and 25 dynamic libraries. We provide methodologies to measure JIT-ROP gadget availability, quality, and their Turing-complete expressiveness, as well as to empirically determine the upper bound of re-randomization intervals in re-randomization schemes using the Turing-complete (TC), priority, MOV TC, and payload gadget sets. Experiments show that the upper bound ranges from 1.5 to 3.5 seconds in our tested applications. Besides, our results show that locations of leaked pointers used in JIT-ROP attacks have no impacts on gadget availability, but have an impact on how fast attackers find gadgets. Our results also show that instruction-level single-round randomization thwarts current gadget finding techniques under the JIT-ROP threat model.