arXiv AI June 26, 2026 By Praneeth Narisetty, Shiva Nagendra Babu Kore, Uday Kumar Reddy Kattamanchi, Jayaram Kumarapu

Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents

arXiv:2606. 26479v1 Announce Type: cross Abstract: Recent work (2024 to 2026) has converged on a strategy for defending tool-using LLM agents against indirect prompt injection: rather than training the model to refuse malicious instructions, enforce security outside the model with a deterministic policy that mediates the agent's actions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 16

AutoDojo: Adaptive Attacks Expose Superficial Defenses and User-Underspecification Limits in LLM Agents

arXiv:2606. 15057v1 Announce Type: cross Abstract: Indirect prompt injection (IPI) is a major security threat to LLM-powered agents.

By Xinhang Ma, Taoran Li, Chaowei Xiao, Zhiyuan Yu, Ning Zhang, Yevgeniy Vorobeychik

llms agents multimodal benchmarks

arXiv AI

4d ago

CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering

arXiv:2609.36570v1 Announce Type: cross Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that...

By Mark Russinovich

llms agents robotics fine-tuning benchmarks

arXiv AI

Aug 25

SecOPD: Mitigating Adaptive Prompt Injections by On-Policy Distillation

arXiv:2608.21500v1 Announce Type: cross Abstract: Prompt injection is listed as the \#1 threat to AI agents. When an agent accesses external data from websites, files, or emails, an attacker may inje...

By Yibo Peng, Long Lian, David Wagner, Sizhe Chen

llms agents reinforcement-learning robotics fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 2

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

arXiv:2606. 02240v1 Announce Type: cross Abstract: 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.

By Hiskias Dingeto, Will Leeney

llms agents multimodal benchmarks safety

arXiv AI

Sep 1

Reachability-Based Capability Confinement for LLM Agents under Indirect Prompt Injection

arXiv:2608.30041v1 Announce Type: cross Abstract: Large language model agents place outputs from external skills into their execution context, allowing attacker-controlled data to influence later pri...

By Wujie Xiong, Rabimba Karanjai, Yang Lu, Weidong Shi, Lei Xu

llms agents benchmarks

arXiv Machine Learning

Jun 25

Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents

arXiv:2605. 08442v3 Announce Type: replace-cross Abstract: Persistent memory attacks against LLM agents achieve high attack success rates against open-source models.

By Jun Wen Leong

llms rag agents multimodal

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Related stories

AutoDojo: Adaptive Attacks Expose Superficial Defenses and User-Underspecification Limits in LLM Agents

CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering

SecOPD: Mitigating Adaptive Prompt Injections by On-Policy Distillation

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

Reachability-Based Capability Confinement for LLM Agents under Indirect Prompt Injection

Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents