IoT devices have brought invaluable convenience to our daily life. However, their pervasiveness also amplifies the impact of security vulnerabilities. Many widespread vulnerabilities of embedded systems reside in their vulnerable border services. Unfortunately, existing vulnerability detection methods can neither effectively nor efficiently analyze such border services: they either introduce heavy execution overheads or have many false positives and negatives. In this paper, we propose a novel static taint checking solution, SaTC, to effectively detect security vulnerabilities in border services provided by embedded devices. Our key insight is that string literals on border interfaces are commonly shared between front-end files and back-end binaries to encode user input. Thus, we extract common keywords from the front-end and use them to locate reference points in the back-end, which indicate the input entry. Then, we apply targeted data-flow analysis to detect dangerous uses of the untrusted user input accurately. We implemented a prototype of SaTC and evaluated it on 39 firmware samples from six popular vendors. SaTC discovered 36 unknown bugs, of which CVE/CNVD/PSV confirms 33. Compared to the state-of-the-art tool KARONTE, SaTC found significantly more bugs in the test set. It shows that SaTC is effective in discovering bugs in embedded systems.

Real-Time Operating System (RTOS) has become the main category of embedded systems. It is widely used to support tasks requiring real-time response such as printers and switches. The security of RTOS has been long overlooked as it was running in special environments isolated from attackers. However, with the rapid development of IoT devices, tremendous RTOS devices are connected to the public network. Due to the lack of security mechanisms, these devices are extremely vulnerable to a wide spectrum of attacks. Even worse, the monolithic design of RTOS combines various tasks and services into a single binary, which hinders the current program testing and analysis techniques working on RTOS. In this paper, we propose SFuzz, a novel slice-based fuzzer, to detect security vulnerabilities in RTOS. Our insight is that RTOS usually divides a complicated binary into many separated but single-minded tasks. Each task accomplishes a particular event in a deterministic way and its control flow is usually straightforward and independent. Therefore, we identify such code from the monolithic RTOS binary and synthesize a slice for effective testing. Specifically, SFuzz first identifies functions that handle user input, constructs call graphs that start from callers of these functions, and leverages forward slicing to build the execution tree based on the call graphs and pruning the paths independent of external inputs. Then, it detects and handles roadblocks within the coarse-grain scope that hinder effective fuzzing, such as instructions unrelated to the user input. And then, it conducts coverage-guided fuzzing on these code snippets. Finally, SFuzz leverages forward and backward slicing to track and verify each path constraint and determine whether a bug discovered in the fuzzer is a real vulnerability. SFuzz successfully discovered 77 zero-day bugs on 35 RTOS samples, and 67 of them have been assigned CVE or CNVD IDs. Our empirical evaluation shows that SFuzz outperforms the state-of-the-art tools (e.g., UnicornAFL) on testing RTOS.

Smart devices without an interactive UI (e.g., a smart bulb) typically rely on specific provisioning schemes to connect to wireless networks. Among all the provisioning schemes, SmartCfg is a popular technology to configure the connection between smart devices and wireless routers. Although the SmartCfg technology facilitates the Wi-Fi configuration, existing solutions seldom take into serious consideration the protection of credentials and therefore introduce security threats against Wi-Fi credentials.