iResolveX: Multi-Layered Indirect Call Resolution via Static Reasoning and Learning-Augmented Refinement

• URL: http://arxiv.org/abs/2601.17888v1
• Date: Sun, 25 Jan 2026 15:42:34 GMT
• Title: iResolveX: Multi-Layered Indirect Call Resolution via Static Reasoning and Learning-Augmented Refinement
• Abstract summary: Indirect call resolution is a key challenge in reverse engineering and control-flow graph recovery.<n>We present iResolveX, a framework that combines conservative static analysis with learning-based refinement.<n>iResolveX supports both conservative, recall-preserving and F1-optimized configurations and outperforms state-of-the-art systems.
• Score: 9.184762707302099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Indirect call resolution remains a key challenge in reverse engineering and control-flow graph recovery, especially for stripped or optimized binaries. Static analysis is sound but often over-approximates, producing many false positives, whereas machine-learning approaches can improve precision but may sacrifice completeness and generalization. We present iResolveX, a hybrid multi-layered framework that combines conservative static analysis with learning-based refinement. The first layer applies a conservative value-set analysis (BPA) to ensure high recall. The second layer adds a learning-based soft-signature scorer (iScoreGen) and selective inter-procedural backward analysis with memory inspection (iScoreRefine) to reduce false positives. The final output, p-IndirectCFG, annotates indirect edges with confidence scores, enabling downstream analyses to choose appropriate precision--recall trade-offs. Across SPEC CPU2006 and real-world binaries, iScoreGen reduces predicted targets by 19.2% on average while maintaining BPA-level recall (98.2%). Combined with iScoreRefine, the total reduction reaches 44.3% over BPA with 97.8% recall (a 0.4% drop). iResolveX supports both conservative, recall-preserving and F1-optimized configurations and outperforms state-of-the-art systems.

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