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1. JVM fuzzing for JIT-induced side-channel detection

   https://doi.org/10.1145/3377811.3380432  

   Brennan, Tegan; Saha, Seemanta; Bultan, Tevfik (June 2020, ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering)

   null (Ed.)

   Timing side channels arise in software when a program's execution time can be correlated with security-sensitive program input. Recent results on software side-channel detection focus on analysis of program's source code. However, runtime behavior, in particular optimizations introduced during just-in-time (JIT) compilation, can impact or even introduce timing side channels in programs. In this paper, we present a technique for automatically detecting such JIT-induced timing side channels in Java programs. We first introduce patterns to detect partitions of secret input potentially separable by side channels. Then we present an automated approach for exploring behaviors of the Java Virtual Machine (JVM) to identify states where timing channels separating these partitions arise. We evaluate our technique on three datasets used in recent work on side-channel detection. We find that many code variants labeled "safe" with respect to side-channel vulnerabilities are in fact vulnerable to JIT-induced timing side channels. Our results directly contradict the conclusions of four separate state-of-the-art program analysis tools for side-channel detection and demonstrate that JIT-induced side channels are prevalent and can be detected automatically. 

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2. TRACED: Execution-aware Pre-training for Source Code

   https://doi.org/10.1145/3597503.3608140  

   Ding, Yangruibo; Steenhoek, Benjamin; Pei, Kexin; Kaiser, Gail; Le, Wei; Ray, Baishakhi (February 2024, ACM)

   Most existing pre-trained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency graphs, etc.). However, program semantics will not be fully exposed before the real execution. Without an understanding of the program execution, statically pre-trained models fail to comprehensively capture the dynamic code properties, such as the branch coverage and the runtime variable values, and they are consequently less effective at code understanding tasks, such as retrieving semantic clones and detecting software vulnerabilities. To close the gap between the static nature of language models and the dynamic characteristics of programs, we introduce TRACED, an execution-aware pre-training strategy for source code. Specifically, we pre-train code language models with a combination of source code, executable inputs, and corresponding execution traces. Our goal is to teach code models the complicated execution logic during the pre-training, enabling the model to statically estimate the dynamic code properties without repeatedly executing code during task-specific fine-tuning. To illustrate the effectiveness of our proposed approach, we fine-tune and evaluate TRACED on three downstream tasks: static execution estimation, clone retrieval, and vulnerability detection. The empirical results show that TRACED relatively improves the statically pre-trained code models by 12.4% for complete execution path prediction and by 25.2% for runtime variable value predictions. TRACED also significantly outperforms statically pre-trained models in clone retrieval and vulnerability detection across four public benchmarks. 

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3. SABER: Identifying SimilAr BEhavioR for Program Comprehension

   Sridhar, Aditya; Qiao, Guanming; Kaiser, Gail (July 2020, Columbia University Computer Science Technical Report cucs-004-20)

   Modern software engineering practices rely on program comprehension as the most basic underlying component for improving developer productivity and software reliability. Software developers are often tasked to work with unfamiliar code in order to remove security vulnerabilities, port and refactor legacy code, and enhance software with new features desired by users. Automatic identification of behavioral clones, or behaviorally-similar code, is one program comprehension technique that can provide developers with assistance. The idea is to identify other code that "does the same thing" and that may be more intuitive; better documented; or familiar to the developer, to help them understand the code at hand. Unlike the detection of syntactic or structural code clones, behavioral clone detection requires executing workloads or test cases to find code that executes similarly on the same inputs. However, a key problem in behavioral clone detection that has not received adequate attention is the "preponderance of the evidence" problem, which advocates for more convincing evidence from nontrivial test case executions to gain confidence in the behavioral similarities. In other words, similar outputs for some inputs matter more than for others. We present a novel system, SABER, to address the "preponderance of the evidence" problem, for which we adapt the legal metaphor of "more likely to be true than not true" burden of proof. We develop a novel test case generation methodology with three primary dynamic analysis techniques for identifying important behavioral clones. Further, we investigate filtering and weighting schemes to guide developers toward the most convincing behavioral similarities germane to specific software engineering tasks, such as code review, debugging, and introducing new features. 

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4. Stealthy Tracking of Autonomous Vehicles with Cache Side Channels

   Luo, Mulong Luo; Myers, Andrew C.; Suh, G. Edward (August 2000, USENIX Security Symposium)

   null (Ed.)

   Autonomous vehicles are becoming increasingly popular, but their reliance on computer systems to sense and operate in the physical world introduces new security risks. In this paper, we show that the location privacy of an autonomous vehicle may be compromised by software side-channel attacks if localization software shares a hardware platform with an attack program. In particular, we demonstrate that a cache side-channel attack can be used to infer the route or the location of a vehicle that runs the adaptive Monte-Carlo localization (AMCL) algorithm. The main contributions of the paper are as follows. First, we show that adaptive behaviors of perception and control algorithms may introduce new side-channel vulnerabilities that reveal the physical properties of a vehicle or its environment. Second, we introduce statistical learning models that infer the AMCL algorithm's state from cache access patterns and predict the route or the location of a vehicle from the trace of the AMCL state. Third, we implement and demonstrate the attack on a realistic software stack using real-world sensor data recorded on city roads. Our findings suggest that autonomous driving software needs strong timing-channel protection for location privacy. 

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5. Stealthy Tracking of Autonomous Vehicles with Cache Side Channels

   Luo, M Luo; Myers, A; Suh, G (August 2020, 29th USENIX Security Symposium (USENIX Security 20))

   Autonomous vehicles are becoming increasingly popular, but their reliance on computer systems to sense and operate in the physical world introduces new security risks. In this paper, we show that the location privacy of an autonomous vehicle may be compromised by software side-channel attacks if localization software shares a hardware platform with an attack program. In particular, we demonstrate that a cache side-channel attack can be used to infer the route or the location of a vehicle that runs the adaptive Monte-Carlo localization (AMCL) algorithm. The main contributions of the paper are as follows. First, we show that adaptive behaviors of perception and control algorithms may introduce new side-channel vulnerabilities that reveal the physical properties of a vehicle or its environment. Second, we introduce statistical learning models that infer the AMCL algorithm's state from cache access patterns and predict the route or the location of a vehicle from the trace of the AMCL state. Third, we implement and demonstrate the attack on a realistic software stack using real-world sensor data recorded on city roads. Our findings suggest that autonomous driving software needs strong timing-channel protection for location privacy. 

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