Influence-Guided Concolic Testing of Transformer Robustness

2026-06-19 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Expert, extended

Summary

Influence-Guided Concolic Testing of Transformer Robustness introduces a novel concolic tester designed for Transformer classifiers. This system prioritizes path predicates using SHAP-based influence estimates to efficiently discover inputs that flip model decisions. It features a solver-compatible, pure-Python semantics for multi-head self-attention and employs practical scheduling heuristics to manage constraint complexity in deeper models. A white-box study on compact Transformers under small L_0 budgets demonstrated that influence guidance outperforms a FIFO baseline, finding label-flip inputs more efficiently and maintaining steady progress on deeper networks. For instance, a single-layer Transformer yielded 67 one-pixel and 6 two-pixel attacks. The approach also revealed recurring, compact decision logic across attacks, with 245 of 4,430 neurons identified as critical for over half of adversarial inputs, suggesting utility for debugging and auditing.

Key takeaway

For AI Security Engineers or ML Engineers focused on Transformer robustness, traditional coverage-driven testing may miss subtle adversarial vulnerabilities. You should consider adopting influence-guided concolic testing, such as the PyCT framework, to efficiently discover decision-changing counterexamples under tight perturbation budgets. Prioritize branches based on SHAP values and apply scheduling heuristics like "prioritized layers" for faster initial findings, or "limited runtimes" to maximize distinct failures on deeper models. This approach provides actionable insights for debugging and model auditing.

Key insights

SHAP-guided concolic testing efficiently finds Transformer vulnerabilities by prioritizing influential decision paths.

Principles

• Influence signals bias symbolic exploration effectively.
• Solver-friendly attention semantics enable Transformer concolic testing.
• Prioritizing high-influence branches improves search precision.

Method

The method integrates SHAP-based influence for path constraint ranking, uses a pure-Python multi-head self-attention semantics, and applies scheduling heuristics like layer prioritization or time-capped constraint building.

In practice

• Prioritize concolic test branches using SHAP-based influence scores.
• Implement Transformer attention in pure Python for SMT solver compatibility.
• Apply layer-prioritized or time-capped scheduling for deep models.

Topics

• Concolic Testing
• Transformer Robustness
• SHAP Explanations
• Adversarial Examples
• Multi-Head Attention
• SMT Solving
• Model Auditing

Code references

• TrustAI/DeepConcolic
• pschanely/CrossHair

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.