New benchmark tests agents' limited access to information
Researchers introduce Partial Evidence Bench, a benchmark to evaluate how AI agents handle restricted access to information within corporate systems, and how this can lead to incomplete responses.
What happened?
A new benchmark, Partial Evidence Bench, has been launched to measure a specific problem with AI agents in enterprise environments. The issue arises when agents operate with restricted access to information, which can result in responses that appear complete even though critical data remains outside the agent's authorisation boundaries. The benchmark contains 72 tasks distributed across three scenarios: due diligence, compliance audits, and security incident response.
Key facts
| Benchmark namn | Partial Evidence Bench |
|---|---|
| Antal uppgifter | 72 |
| Scenarier | Due diligence, compliance-revision, säkerhetsincidenthantering |
| Publiceringsdatum | 2026-05-09 |
”Enterprise agents increasingly operate inside scoped retrieval systems, delegated workflows, and policy-constrained evidence environments. In these settings, access control can be enforced correctly while the system still produces an answer that appears complete even though mater”
”Checked-in baselines show that silent filtering is catastrophically unsafe across”
Why it matters
This benchmark addresses a critical security and reliability issue for AI agents used in business. When agents provide incomplete answers because they lack permission for certain information, without signalling this limitation, there is a risk of flawed decision-making. The goal is to identify and resolve "silent filtering" where relevant information is excluded unnoticed, which can have serious consequences in business-critical contexts.
Who is affected?
Developers of AI agents and decision-makers implementing agent-based systems in enterprises are directly affected. Companies investing in or relying on AI agents for tasks like due diligence, regulatory compliance, and incident management gain a tool to evaluate and improve their systems. End-users within these companies also benefit indirectly through the enhanced reliability of the responses produced by AI systems.
What else you should know
Existing baselines indicate that silent filtering is "catastrophically unsafe" across all tested surfaces, underlining the need for this type of evaluation tool.
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