Dean Sullivan | Search arXiv

arXiv2026

From Silicon to Boot Code: Extending Automated Program Repair to Firmware-Layer Security Workarounds

Automated program repair (APR) research has been constrained to design time. Current techniques localize and fix bugs in RTL or HLS designs before a chip reaches production. Once a hardware vulnerability surfaces post-silicon, the patch must be manually generated: existing automation addresses patch deployment but not patch synthesis. We study the feasibility of extending a dictionary-guided, localize-synthesize-validate APR methodology originally developed for RTL repair to this firmware layer. An automated commit-clustering miner surfaces recurring fix templates across the EDK II (UEFI) firmware repository's full commit history without depending on known CVE identifiers, recovering all three known CVE-fix campaigns and surfacing two additional candidate bug families. Grounded in real fix evidence, we build four independent localizers: missing speculation barriers in C (CVE-2017-5753, Spectre v1), missing bounds checks before array writes in C (a decompression library CVE), missing Return Stack Buffer stuffing in x86 assembly (CVE-2017-5715), and missing integer-overflow guards in Hand-Off Block creation code (surfaced by the miner itself). All four achieve 100% recall; precision ranges from 2.1-15.5% on the C families to 100% on the assembly and HOB families. Root-cause analysis of the C-family false positives attributes 77-90% to two intra-procedural causes, isolating the inter-procedural alias-analysis gap as a measured 15-20% rather than an estimate. A held-out test confirms Spectre v1 localization holds at 100% recall on unseen files; a fifth, independently built dictionary entry (CVE-2018-3630) shows the methodology extends to a new bug signature at low cost; and a naive syntactic baseline recalls at most 14% where our detector recalls 100%. We frame these results within a broader research agenda for a unified hardware-to-firmware correctness lifecycle.

cs.SE↗

arXiv2026

Dictionary-Guided Mutation Operators for Automated HDL Repair

Automated repair of Hardware Description Language (HDL) designs remains challenging due to the large search space of candidate repairs and the strict syntactic and semantic constraints imposed by HDL grammars. Generic mutation strategies overwhelmingly generate syntactically invalid candidates that waste compilation and simulation budget, while synthesis-driven and template-based approaches impose their own constraints on generality and portability. In this paper, we propose a dictionary-guided HDL repair system that combines ANTLR-derived DUT-specific mutation vocabularies with a simulation-divergence fault localization (FL) module. The mutation operator applies category-constrained token substitutions, insertions, and deletions directly to Verilog source via regex-based matching, without requiring AST manipulation or synthesis. The FL module identifies diverging output wires from a single simulation run and scores source lines by structural proximity to those signals, directing the mutation search toward high-suspicion regions. A deterministic targeted sweep exhausts all dictionary mutations on the highest-scored lines before falling back to a genetic programming (GP) search. Evaluated on the CirFix benchmark suite across six design under test (DUT) families, the proposed approach produces correct oracle-passing repairs on 14 bug variants, including a 6-edit multi-bug instance that CirFix cannot repair, and achieves an 18x speedup over CirFix on a two-edit benchmark variant. These results indicate that dictionary-constrained mutation operators, combined with lightweight simulation-divergence FL, are a practical and competitive approach to automated HDL repair for common bug classes without formal analysis or synthesis dependencies.

cs.ET↗