AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations
Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail, Salesforce, or Jira accessed through tool calls) whose response content the user neither writes nor controls. Existing benchmarks under-measure the threat: most cover only a handful of integrations with the same attack payload replayed across runs, and open-source guards are trained on chat-style data rather than tool-response content. We introduce AGENTREDBENCH, a dynamic LLM-driven redteaming benchmark of 215 subtle underspecified-authorization scenarios across 24 enterprise integrations and five attack types. Across an eight-model panel (Anthropic, OpenAI, Google), no-guard attack success rate ranges from 32% to 81%. To keep the scenario set out of training corpora and preserve headline ASR meaning over time, we release the codebase, integration schemas, and AGENTREDGUARD model openly; the canonical scenarios are evaluated through a maintainer-mediated channel with immutable versioning. AGENTREDGUARD cuts online attack success by 75-77pp across three target model families (Haiku, GPT-5.4-mini, Gemini-3-flash) at 0.0% real-benign false-positive rate (0.2% on a synthetic-benign corpus), outperforming every open-source baseline with non-trivial detection (Llama Guard, PromptGuard 2, ProtectAI) on both axes. Cross-integration and cross-attacker holdouts (two independent attacker families held out from training) confirm the gain transfers beyond the training subset.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties
Development of autonomous cyber system defense strategies and action recommendations in the real-world is challenging, and includes characterizing system state uncertainties and attack-defense dynamics. We propose a data…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning
Decentralized Federated Learning (DFL) remains highly vulnerable to adaptive backdoor attacks designed to bypass traditional passive defense metrics. To address this limitation, we shift the defensive paradigm toward a n…
Federated LearningAgentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning
Unmanned Aerial Vehicles (UAVs) are increasingly used in defense, surveillance, and disaster response, yet most systems still operate at SAE Level 2 to 3 autonomy. Their dependence on rule-based control and narrow AI lim…
Object DetectionAdversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization
Large Language Models (LLMs) have developed rapidly in web services, delivering unprecedented capabilities while amplifying societal risks. Existing works tend to focus on either isolated jailbreak attacks or static defe…
Reinforcement LearningAdversarial AttackDon't Click That: Teaching Web Agents to Resist Deceptive Interfaces
Vision-language model (VLM) based web agents demonstrate impressive autonomous GUI interaction but remain vulnerable to deceptive interface elements. Existing approaches either detect deception without task integration o…