LightSLH: Provable and Low-Overhead Spectre v1 Mitigation through Targeted Instruction Hardening
<|reference_start|>LightSLH: Provable and Low-Overhead Spectre v1 Mitigation through Targeted Instruction Hardening: Several software mitigations have been proposed to defend against Spectre vulnerabilities. However, these countermeasures often suffer from high performance overhead, largely due to unnecessary protections. We propose LightSLH, designed to mitigate this overhead by hardening instructions only when they are under threat from Spectre vulnerabilities. LightSLH leverages program analysis techniques based on abstract interpretation to identify all instructions that could potentially lead to Spectre vulnerabilities and provides provable protection. To enhance analysis efficiency and precision, LightSLH employs novel taint and value domains. The taint domain enables bit-level taint tracking, while the value domain allows LightSLH to analyze complex program structures such as pointers and structures. Furthermore, LightSLH uses a two-stage abstract interpretation approach to circumvent potential analysis paralysis issues.   We demonstrate the security guarantees of LightSLH and evaluate its performance on cryptographic algorithm implementations from OpenSSL. LightSLH significantly reduces the overhead associated with speculative-load-hardening techniques. Our results show that LightSLH introduces no protection and thus no overhead on 4 out of the 7 studied algorithms, which contrasts with existing countermeasures that introduce additional overhead due to unnecessary hardening. Additionally, LightSLH performs, for the first time, a rigorous analysis of the security guarantees of RSA against Spectre v1, highlighting that the memory access patterns generated by the scatter-gather algorithm depend on secrets, even for observers at the cache line granularity, necessitating protection for such accesses.<|reference_end|>
@article{zhu2024lightslh:,
    title={LightSLH: Provable and Low-Overhead Spectre v1 Mitigation through
  Targeted Instruction Hardening},
    author={Yiming Zhu, Wenchao Huang, Yan Xiong},
    journal={arXiv preprint arXiv:2408.16220},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16220},
    primaryClass={cs.CR}
}