Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines

📰 ArXiv cs.AI

Learn to expose architectural vulnerabilities in black-box NLP pipelines using agentic adversarial rewriting, a crucial skill for AI security and robustness

advanced Published 28 Apr 2026

Action Steps

Formalize a black-box threat model for NLP pipelines
Implement a two-agent evasion framework using semantic perturbation space
Train an Attacker Agent to generate meaning-preserving rewritings
Evaluate the robustness of NLP pipelines under realistic conditions
Apply agentic adversarial rewriting to expose architectural vulnerabilities

Who Needs to Know This

NLP engineers, AI security researchers, and data scientists can benefit from this knowledge to improve the robustness of their NLP pipelines and identify potential vulnerabilities

Key Insight

💡 Agentic adversarial rewriting can effectively test the robustness of NLP pipelines under realistic conditions, such as binary-only feedback and strict query budgets

Key Takeaways

Learn to expose architectural vulnerabilities in black-box NLP pipelines using agentic adversarial rewriting, a crucial skill for AI security and robustness

Full Article

Title: Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines

Abstract:
arXiv:2604.23483v1 Announce Type: new Abstract: Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can test their robustness under realistic conditions: binary-only feedback, no gradient access, and strict query budgets. We formalize this strict black-box threat model and propose a two-agent evasion framework operating in a semantic perturbation space. An Attacker Agent generates meaning-preserving r

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