Fetching from the wire…
Public story · 2026-08-25 · high
It tracks where data moves through an agent instead of judging one action at a time, and task success rose to 63.3% from 46.7%.
Why now: AgentFlow posted to arXiv in August 2026, testing against three separate agent-safety benchmarks that already stand in for how deployed agents get attacked with prompt injection.
A new policy language pushed confirmed agent compromise to zero, down from 33%, according to the AgentFlow paper.
That risk applies to anyone running an agent with access to files, email, or code. On AgentDojo's 949 test cases, agents defended by current methods still got compromised 33% of the time.
Most agent defenses judge each action on its own, asking whether one file read or one API call looks dangerous. AgentFlow tracks where data is allowed to move through the whole system instead. It combines flow and path rules, capabilities scoped to a single task, controlled release points, and taint tracking that persists across steps. A runtime monitor enforces the rules, and a separate verifier checks seven safety properties against them with SMT solving, each in under half a second.
On AgentDojo, that combination cut compromise to 0.0% and lifted task success to 63.3%, up from 46.7%. AgentDyn Dailylife's compromise rate dropped from 73.5% to 0.0% with utility essentially flat, and none of ASB's 1,200 direct prompt injection attempts got through.
The paper doesn't say how the policies get written for a new agent. It also doesn't say how much engineering work that takes before an app can plug the system in. Checking seven properties in under half a second only helps if someone declared the right properties to check.
Each link below shares sources, entities, or timing with this story.
arXiv 2608.11878 replaces the handful of manually implemented injection-testing environments with an Environment Simulator, Attacker Agent, and User Simulator that generate executable stateful environments and discover viable injection points automatically. Injection timing an...
A July 21 paper pairs two near-identical agents: an Explore Agent that inspects untrusted input but holds no tools, and a Safe Agent that takes privileged actions using its own context plus length-constrained hints from the explorer (arXiv 2607.19595). Borrowing from residual...
The Agentic Principal Chain (arXiv 2608.15888) reframes prompt injection as an authorization problem: an injection only matters if the agent holds authority to act on it. Across 3,154 instances spanning InjecAgent, AgentDojo, and ASB, exfiltration hit 0% in all four AgentDojo...
StepGuard (arXiv 2608.24777) is an open-weight guard model auditing individual tool calls pre-execution rather than scoring completed trajectories, which is where most guardrails sit. It trains on paired safe and unsafe trajectories that share identical context and diverge onl...
arXiv 2608.05108 skips the RL-trained attacker models that dominate red teaming and generalize poorly, instead accumulating a strategy library across a sequence of (dataset, target) pairs that transfers to unseen targets with no retraining. AgentDojo: 86.7% ASR against Gemini-...
First defense modeling multi-turn indirect prompt injection as temporal causal takeover. Uses counterfactual re-executions at tool-return boundaries to detect when tool outputs steer agent behavior. Evaluated on AgentDojo across four task suites. Builder-ready pattern for tool...
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