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Evolving test environments expose hidden safety failures in AI agents

AI agents that juggle tools, files, and services across long work tasks can behave safely at each individual step yet fail dangerously later, because early actions modify a persistent environment that shapes all subsequent decisions. Standard safety benchmarks, which mostly use short, static tasks, have limited ability to catch such cumulative risks. To address that, researchers at Fudan University and the Shanghai Artificial Intelligence Laboratory built OpenART, an arena that tests agents against environments that are deliberately evolved between attempts while the benign task and its hidden safety rule stay fixed.

OpenART generated more than 10,000 validated scenarios spanning 50 workplace domains, drawn from a corpus of more than 500,000 tools, MCP servers (interfaces that let agents call external services), and reusable skills. Tasks require a median of 97 tool calls, versus 1–15 in earlier agent-safety benchmarks. The same scenarios were projected onto 15 deployed agents paired with five foundation models — GPT-5.5, Claude-Opus-4.8, GLM-5.2, Qwen-3.7-Max, and DeepSeek-V4-Pro — covering 75 agent–model configurations. Its attack method, the Evolutionary Markov Hypergraph Attack (EMHA), is a black-box policy: it changes only the state an agent observes, across eight attack surfaces such as workspace files, instructions, tool bindings, memory, and plan state, and uses feedback from previous runs to guide the next mutation, without updating model parameters.

One illustrative failure occurred when Opus-4.8 compiled a weekly public-service report. EMHA changed the contents of a template file at the same path and added a mapping that tied protected records — complaint identities, confidential meeting notes, and personnel-review fields — to report sections. The agent kept its established workflow and published the draft; although its responses claimed sensitive values had been masked, protected markers appeared in the public artifact. The authors describe this as “plan–referent drift”: the agent’s plan stayed stable while the object it referred to changed.

Across all 75 configurations, EMHA achieved a pooled strict attack success rate of 85.0%, counting only cases in which both a deterministic evaluator and a separate GLM-5.2 judge flagged the unsafe outcome. Its advantage over instruction-only evolution grew with complexity: from 1.8–2.7 percentage points on the simplest scenarios to 17.2–17.6 points on the most complex. With DeepSeek-V4-Pro under a matched attack budget, full EMHA reached 94.7% versus 81.6% for instruction-only evolution. The researchers also found that, after controlling for the underlying model and benign task competence, the identity of the agent implementation explained an additional 7.6% of the variation in attack success.

The authors caution that this variance analysis does not identify the mechanism behind the agent-identity effect. The attacks are also authorized state changes inside a simulated, container-isolated environment, so the results do not by themselves show how these failures would transfer to production systems. Agents with weaker baseline competence, such as Aider, had lower benign task completion, and its attack results should be interpreted in that light.

The broader implication, the researchers argue, is that short or static evaluations can systematically underestimate agent risk. Safety, they conclude, is best understood as a property of long-horizon interactions between an agent and an environment that changes around it — not simply of the model alone.