Web applications widely use the logging functionality, but improper handling can bring serious security threats. An attacker can trigger the execution of malicious data by writing malicious data to the web application logs and then accessing the view–logs interface, resulting in a vulnerability of the web application log injection. However, detecting this type of vulnerability requires automatic discovery of log-injectable interfaces and view–logs interfaces, which is difficult. In addition, bypasssing the application-specific input-filtering checks to write an effective payload to the log is also challenging. This paper proposes LogInjector, an efficient web application log injection vulnerability detection method. First, it obtains the log storage form and location and then finds the log-injectable interfaces through the extended dynamic crawler. Second, it automatically identifies the web application view–logs interfaces. Finally, LogInjector utilizes a dynamic testing approach based on the feedback-guided mutation to detect web application log injection vulnerabilities. To verify the effectiveness of LogInjector, we test it in 14 popular web applications in real-world cases and compare it with Black Widow, the state-of-the-art web vulnerability scanner. LogInjector detects 16 web application log injection vulnerabilities, including 6 zero-day vulnerabilities, while Black Widow can only detect three log injection vulnerabilities, demonstrating the effectiveness of LogInjector in practice.

Modern web services widely provide RESTful APIs for clients to access their functionality programmatically. Fuzzing is an emerging technique for ensuring the reliability of RESTful APIs. However, the existing RESTful API fuzzers repeatedly generate invalid requests due to unawareness of errors in the invalid tested requests and lack of effective strategy to generate legal value for the incorrect parameters. Such limitations severely hinder the fuzzing performance. In this paper, we propose DynER, a new test case generation method guided by dynamic error responses during fuzzing. DynER designs two strategies of parameter value generation for purposefully revising the incorrect parameters of invalid tested requests to generate new test requests. The strategies are, respectively, based on prompting Large Language Model (LLM) to understand the semantics information in error responses and actively accessing API-related resources. We apply DynER to the state-of-the-art fuzzer RESTler and implement DynER-RESTler. DynER-RESTler outperforms foREST on two real-world RESTful services, WordPress and GitLab with a 41.21% and 26.33% higher average pass rate for test requests and a 12.50% and 22.80% higher average number of unique request types successfully tested, respectively. The experimental results demonstrate that DynER significantly improves the effectiveness of test cases and fuzzing performance. Additionally, DynER-RESTler finds three new bugs.