Abstract
Abstract
Firmware, the low-level software embedded in hardware devices, is critical for system security and functionality. Compromised firmware can have severe consequences, particularly as the prevalence of IoT and embedded systems increases. Traditional software security techniques are often inadequate due to the binary nature, resource constraints, and close hardware interaction inherent to firmware. This paper extends the "Code Canary" concept, proposing a novel adaptation for firmware integrity verification using AI-driven techniques to generate binary canaries. We explore the generation, embedding, and detection of these canaries within firmware images using binary analysis techniques such as Control Flow Graphs (CFGs) and Data Flow Graphs (DFGs). We demonstrate the feasibility of this approach and discuss its advantages in detecting firmware tampering, even in the presence of code modifications and obfuscation. We also address firmware-specific adversarial attack vectors and propose mitigation strategies, emphasizing the defender's advantage in generating and identifying AI-driven canaries. This work represents a significant step towards securing firmware and protecting critical infrastructure.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Van Hoose, Ray, "AI Code Canaries for Binaries and Firmware", Technical Disclosure Commons, (January 02, 2025)
https://www.tdcommons.org/dpubs_series/7696