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This paper addresses a critical flaw in evaluating tool-using AI agents by proposing executable task contracts to rigorously define what constitutes the same task during robustness testing. By validating test case edits against specific factual definitions rather than weak embedding thresholds, the authors demonstrate that current methods often misclassify benign variations as attacks or miss genuine vulnerabilities. This work is essential for establishing reliable benchmarks and ensuring that security evaluations of autonomous agents reflect true model behavior rather than artifacts of poor test design.
What Counts as the Same Task? Executable Contracts for Robustness Evaluation of Tool-Using Agents
arXiv:2610.14477v1 Announce Type: cross Abstract: Robustness tests for tool-using agents often change the agent's environment, for example by hiding a hostile note in an invoice, and then judge the agent by what it does. The result tells us about the original task only if the changed test case is still a valid test of that task. Many evaluations decide this with weak rules, such as accepting any well-formed edit or any edit that stays close to the original in embedding space. We propose executable task contracts. A contract states which facts define a task, which edits are allowed, and what finishing the task means, and it checks each changed test case before the agent runs. We compare validity rules by scoring the same 14,336 stored agent runs under each rule. Across four encoders, embedding thresholds allowed 75-100% of edits that changed the task, such as swapping the payee, yet rejected 24-45% of valid hostile notes. With a MiniLM cosine threshold, Mistral appeared vulnerable on all 64 tasks, but only 26 had a successful valid attack. Replaying a saved solution and allowing edits to benchmark fields fail in similar ways. Evaluations should state, and check, what counts as the same task.
Title:What Counts as the Same Task? Executable Contracts for Robustness Evaluation of Tool-Using Agents
Abstract:Robustness tests for tool-using agents often change the agent's environment, for example by hiding a hostile note in an invoice, and then judge the agent by what it does. The result tells us about the original task only if the changed test case is still a valid test of that task. Many evaluations decide this with weak rules, such as accepting any well-formed edit or any edit that stays close to the original in embedding space. We propose executable task contracts. A contract states which facts define a task, which edits are allowed, and what finishing the task means, and it checks each changed test case before the agent runs. We compare validity rules by scoring the same 14,336 stored agent runs under each rule. Across four encoders, embedding thresholds allowed 75-100% of edits that changed the task, such as swapping the payee, yet rejected 24-45% of valid hostile notes. With a MiniLM cosine threshold, Mistral appeared vulnerable on all 64 tasks, but only 26 had a successful valid attack. Replaying a saved solution and allowing edits to benchmark fields fail in similar ways. Evaluations should state, and check, what counts as the same task.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
arXiv:2610.14477 [cs.SE]
(or arXiv:2610.14477v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2610.14477 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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