Research · White Paper

Securing Tool-Using AI Agents

Fisher is Mace Ai’s adversarial testing platform for configured, tool-using agents. This paper explains why transcript-only evaluation misses real failures, and how Fisher uses action evidence, replay, remediation re-attack, and strategy learning to prove what happened.

50,000+
research episodes
500,000+
agent turns analyzed
20+
model architectures
Hundreds
retained strategies

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M
Securing
Tool-Using
AI Agents
Mace Ai
white paper
2026
M
Download
the paper

Open the PDF for the full Fisher method, evidence model, and adaptive testing results.

Mace Ai
research
What the paper examines

Why transcripts alone can’t clear an agent:

Tool-using agents hold standing access to files, mail, databases, and internal APIs. A fluent refusal can look safe in the transcript while the action trace shows the agent already queried protected records, wrote to a tool, or crossed a workflow boundary.

The paper documents that blind spot with a controlled head-to-head on the same database agent. It then lays out Fisher’s assurance loop for adaptive, multi-turn campaigns against an explicit target contract.

01 / Probe

Find the boundary

Run adaptive conversations against configured tools, data, policies, and workflows.

02 / Prove

Use action evidence

Evaluate tool calls, retrievals, writes, and boundary crossings instead of relying on final text.

03 / Self-learning

Verify the fix

Record remediation verdicts as fixed, partial, bypassed, or unknown and feed retained strategies back into future authorized campaigns.