Large Language Model-Powered Protected Interface Evasion: Automated Discovery of Broken Access Control Vulnerabilities in Internet of Things Devices

Broken access control vulnerabilities pose significant security risks to the protected web interfaces of IoT devices, enabling adversaries to gain unauthorized access to sensitive configurations and even use them as stepping stones for attacking the intranet. Despite its ranking as the first in the latest OWASP Top 10, there remains a lack of effective methodologies to detect these vulnerabilities systematically. We present ACBreaker, a novel methodology powered by a large language model (LLM), to effectively identify broken access control vulnerabilities in the protected web interfaces of IoT devices. Our methodology consists of three stages. The initial stage transforms firmware code that exceeds the LLM context window into semantically intact code snippets. The second stage involves using an LLM to extract device-specific information from firmware code. The final stage integrates this information into the mutation-based fuzzer to improve fuzzing effectiveness and employ differential analysis to identify vulnerabilities. We evaluated ACBreaker across 11 IoT devices, analyzing 1,274,646 lines of code and discovering 39 previously unknown vulnerabilities. We further analyzed these vulnerabilities, categorizing them into three types that contribute to protected interface evasion, and provided mitigation suggestions. These vulnerabilities were responsibly disclosed to vendors, with CVE IDs assigned to those in six IoT devices.