AgentDyn (Product): news timeline & CVEs

Climbing the Hill: Prompt Injection Red-Teaming Against Frontier Models with Curriculum Reinforcement Learning

Curriculum RL red-teams frontier LLMs for prompt injection, hitting 93.8% ASR against GPT-5.6-Luna on AgentDyn.

Researchers propose curriculum reinforcement learning to red-team frontier LLMs for prompt injection, avoiding a cold start where every attempt scores zero reward. The attacker is trained on a sequence of increasingly robust targets and must partially succeed at each stage. On AgentDyn, the method reached ASR@10 of 93.8% against GPT-5.6-Luna and 45.0% against GPT-5.6-Terra, while RL-Hammer and PISmith scored 0%. An attacker trained on GPT-5.6-Terra also transferred to six other frontier models, including GPT-6-Luna; code is released as PIForge.

arXiv cs.CRSep 27, 14:44 UTC · 2d agoAI safety & security

ActGov: Governing LLM Agent Actions via Policy-Constrained Validation

ActGov validates each LLM agent tool call at runtime, cutting indirect prompt-injection success while preserving task utility.

Researchers present ActGov, a runtime framework that validates every LLM-proposed tool action before it has external effects. ActGov-Policy iteratively builds policies from tool specifications, benign tasks, and failure traces, verifying each update with SMT-based counterexample checking. At runtime, a call is allowed only if it stays inside the task-scoped authorization boundary and satisfies applicable policies. On AgentDojo and AgentDyn, across multiple models and attack setups, ActGov reduced indirect prompt-injection success while preserving task utility and outperformed existing defenses.

arXiv cs.CRSep 21, 11:47 UTC · 8d agoAI safety & security1

AgentDyn

Timeline2 articles

Appears with

Climbing the Hill: Prompt Injection Red-Teaming Against Frontier Models with Curriculum Reinforcement Learning

ActGov: Governing LLM Agent Actions via Policy-Constrained Validation