AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations

2026-04-20 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Expert, extended

Summary

AgentRedBench is a new dynamic LLM-driven redteaming benchmark designed to evaluate indirect prompt injection threats in LLM agents operating over SaaS integrations. It features 215 subtle underspecified-authorization scenarios across 24 enterprise integrations from nine functional families and five attack types, including 49 multi-integration chained scenarios. An evaluation across an eight-model panel (Anthropic, OpenAI, Google) revealed no-guard attack success rates (ASR) ranging from 32% for Claude Sonnet 4.6 to 81% for Gemini 3 Flash. To counter this, the researchers developed AgentRedGuard, a finetuned classifier trained on integration-diverse adversarial tool-response content. AgentRedGuard significantly reduced the panel's ASR from 69.9% to 2.4% at a 0.37% false-positive rate, outperforming existing open-source baselines. It operates with a median CPU latency of 9.5 ms, demonstrating strong cross-integration and cross-attack-type generalization.

Key takeaway

For AI Security Engineers deploying LLM agents with SaaS integrations, you must recognize the severe threat of indirect prompt injection through tool responses. Your existing general-purpose LLM guards are likely insufficient. You should integrate specialized defenses like AgentRedGuard, which significantly reduces attack success rates with minimal latency. Furthermore, you should adopt dynamic redteaming methodologies to continuously evaluate your agents against subtle, integration-aware attacks, ensuring robust protection beyond static benchmarks.

Key insights

Indirect prompt injection via SaaS integrations poses a severe, under-measured threat to LLM agents, requiring specialized defenses.

Principles

• LLM agents face severe indirect prompt injection threats via SaaS integrations.
• General-purpose LLM guards are ineffective against tool-response content.
• Dynamic redteaming is essential for robust agent security evaluation.

Method

AgentRedBench uses an attacker LLM to generate per-run injection content for mock integration states. A target agent runs, optionally with a guard, and an LLM judge scores the trace against success criteria.

In practice

• Deploy AgentRedGuard to filter adversarial tool-response content.
• Conduct dynamic redteaming with varied attack types and integrations.
• Explicitly bind agent write actions to original message sources.

Topics

• LLM Agents
• Indirect Prompt Injection
• SaaS Integrations
• Red Teaming
• Agent Security Benchmarks
• Guard Models
• Attack Surface Analysis

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

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