AI agents did not become less safe simply because they used tools. A 2026 study found a narrower risk: in the tested setups, schema-formatted tool specifications appeared to weaken models’ refusal signals and contribute to unsafe tool execution. Its proposed mitigation, SafeKeep, uses a flattened text representation to assess requests while retaining the original schemas for execution.
What the study found about tool use and safety
In “Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents,” Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan, Yu Jiang and Zhenpeng Chen identify schema-formatted tool specifications as a source of safety degradation. The authors say their white-box representation analysis found that these specifications weaken internal refusal signals, contributing to unsafe tool execution. The paper was submitted to arXiv on July 31, 2026. Read the paper abstract.
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The concern is about how tool instructions are represented to a model, not proof that all tool use makes all agents less safe. A schema describes a tool’s structure and parameters in a machine-readable format. The study’s proposed explanation is that this representation can affect the model’s ability to recognize and refuse harmful requests in the evaluated setting.
How SafeKeep is intended to work
SafeKeep separates the representation used for safety assessment from the representation used to invoke tools. It evaluates a request using a flattened, textual version of the tool specifications, but keeps the original schema-formatted specifications for execution. In other words, the method changes what the agent uses to judge whether a request is safe without replacing the structured format used to call the tool.
#1 Best Overall
The abstract reports evaluations on two representative benchmarks and four LLMs, including white-box and black-box models. It says SafeKeep preserved task-handling capability and outperformed existing safeguards, but the abstract alone does not provide enough detail to assess those comparisons or the specific models and benchmarks.
Reported results—and what they do and do not show
| Measure | Reported result | Scope |
|---|---|---|
| Average refusal of harmful requests | Increased from 23.8% to 70.6% | Average reported by Pan et al. (2026) across the paper’s evaluation |
| Average attack success under observation-level prompt injection | Decreased from 25.6% to 2.5% | Average reported by Pan et al. (2026) across the paper’s evaluation |
These are results reported by the paper for its tested benchmarks and models, not universal rates for deployed AI agents. The abstract does not establish that the method works across every tool format, agent architecture, or real-world deployment, nor does it show that SafeKeep guarantees safety. The detailed experimental breakdown and limitations are not established by the abstract.
How the finding fits into practical agent security
Tool representation is only one part of an agent’s security boundary. In separate practitioner guidance, NVIDIA AI Red Team describes recurring failure modes found in its assessments: weak access control, tools that permit arbitrary code execution, missing network-egress controls, and plaintext secrets exposed to agents. Its suggested mitigations include restricting access externally, sandboxing, default-deny network egress, and keeping secrets out of an agent’s reach. These are deployment recommendations, not SafeKeep’s mechanism or findings. Read NVIDIA’s AI Red Team guidance.
NVIDIA also announced its Open Agent Safety Platform on September 28, 2026, describing OpenShell software and a Sentry reference design for governance and control across agent software, compute, hardware, and robotics. That announcement is separate company context; it is not evidence that SafeKeep is part of the platform or that the paper’s results were evaluated there. Read NVIDIA’s announcement.
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What to look for when evaluating an agent safeguard
The paper makes tool-description format a concrete issue to test, rather than assuming that a safeguard that blocks unsafe outputs will behave the same way with every tool interface. For a meaningful comparison, check:
Quick Recap
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- Which representation the safeguard evaluates: schema, flattened text, or another format.
- Whether safety judgment is separated from the representation used to execute tool calls.
- Refusal rates on harmful requests and attack success under prompt injection.
- Whether ordinary task-handling capability is retained.
- Which models, benchmarks, and deployment conditions support the reported results.
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