TaintFuzzer: SoC Security Verification using Taint Inference-enabled Fuzzing
Muhammad Monir Hossain, Nusrat Farzana Dipu, Kimia Zamiri Azar, Fahim Rahman, Farimah Farahmandi, Mark Mohammad Tehranipoor · 2023
Modern System-on-Chip (SoC) designs containing sensitive information have become targets of malicious attacks. Unfortunately, current verification practices still undermine the importance of SoCs security verification due to extreme time-to-market constraints, lack of autonomous methodologies, and low coverage. This results in SoC designs moving forward to production with security holes, making them insecure and exploitable by adversaries. Traditional taint analysis and formal approaches are losing applicability to industrial applications due to labor-intensive, slow, and scalability issues. Some approaches apply fuzz testing for hardware vulnerability detection using state-of-the-art software fuzzers, also utilizing information flow tracking for better coverage. However, these approaches prove to be inefficient and cannot be applied to SoCs integrated with third-party IPs (3PIP) for several reasons: laborious white-box-based taint analysis, inconsiderate cross-layer co-verification, and lacking hardware-centric input mutations. This paper proposes Taintuzzer, a fuzzing-driven automated SoC security verification framework leveraging taint inference (feasible in gray-box verification) for detecting SoC security vulnerabilities. Unlike previous studies relying on traditional (code) coverage-related metrics, in TaintFuzzer, we develop (i) schemes for generating smart seeds, (ii) a security-oriented cost function, and (iii) run-time feedback for the mutation engine to choose the appropriate strategies to mutate stimuli targeting SoC modules. TaintFuzzer is powered by FPGA emulation of SoC, making it extremely fast and scalable, especially for cross-layer co-verification. TaintFuzzer's cost function and feedback enable dynamic tuning of mutation strategies to generate hardware-centric inputs. Our experiments with RISC-V-based SoC demonstrate the TaintFuzzer's effectiveness in detecting both known and unknown vulnerabilities in significantlv less time.