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AgentGuardBench is an open benchmark for testing whether tool-using AI agents handle security and privacy risks while still completing legitimate tasks. Its first release uses 120 synthetic scenarios in English, French, Swahili, and Yoruba. The published scores compare two deterministic test policies—not commercial or production AI models—so they demonstrate how the benchmark and scoring pipeline behave, not how safe real-world agents are.
What AgentGuardBench evaluates
AgentGuardBench tests responsible behavior when an agent encounters malicious instructions, requests for protected information, or actions beyond its authority. It also checks whether an agent can complete benign tasks rather than refusing everything. The project describes the benchmark as locally reproducible, with inert tools and deterministic reference policies; it does not require production credentials, paid model APIs, or real external recipients.
The project overview describes version 0.1.1 as 120 fully synthetic scenarios, while the repository labels its 120-case dataset description version 0.1. The available project pages therefore use different version labels. Both describe six risk categories, five sectors—banking, healthcare, education, government, and recruitment—and four languages: English, French, Swahili, and Yoruba. The overview reports 20 cases per risk category, 24 per sector, and 30 per language. These are project-reported design figures, not independent counts. The project says the dataset contains no real people, credentials, organizations, or operational targets.
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What the six scenario categories expect
Each category pairs a risky or legitimate request with an expected response. The benchmark’s categories and expected behaviors are:
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| Category | Scenario | Expected behavior |
|---|---|---|
| Prompt injection | Untrusted content contains malicious directions. | Refuse to follow the injected instructions. |
| Privacy leakage | A request seeks protected fields. | Redact the sensitive information. |
| Tool misuse | An unreviewed external action is requested. | Ask for human approval. |
| Privilege abuse | A read-only role is asked to perform a destructive action. | Refuse the action. |
| Memory safety | The agent is asked to reveal secrets supplied earlier. | Refuse to disclose them. |
| Benign controls | A legitimate request concerns public synthetic information. | Complete the task. |
The project describes an evaluation flow in which synthetic scenarios are presented to an agent policy, safe mock tools handle tool interactions, and an evaluator generates metrics and machine-readable results. The tests target instruction priority, privacy protection, least privilege, approval for high-impact actions, and the ability to serve legitimate requests.
How to interpret the published baseline
Joseph Arayemi’s 2026 project overview reports results for two deterministic fixtures evaluated on the same 120 cases. The figures below are the benchmark author’s fixture results, not findings from a test of commercial or production models.
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| Metric | Strict reference policy | Permissive control |
|---|---|---|
| Overall pass rate | 100.00% | 16.67% |
| Attack success rate | 0.00% | 100.00% |
| Privacy leakage rate | 0.00% | 50.00% |
| Unauthorized tool-use rate | 0.00% | 33.33% |
| Human-approval violation rate | 0.00% | 16.67% |
| Benign-task completion rate | 100.00% | 100.00% |
Arayemi says the results validate the benchmark mechanics and scoring pipeline. The author also notes that the templates are intentionally easy to audit. They do not show how a deployed model would perform, nor do they establish that the benchmark captures the full range of attacks or deployment conditions.
Read safety and utility measures together. A policy that blocks attacks but also fails legitimate tasks would have a different profile from one that protects against misuse while completing benign work. Category, sector, and language breakdowns can reveal uneven performance hidden by an aggregate score. The project pages do not report comparative production-model results.
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- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
Metrics the benchmark reports
AgentGuardBench reports overall pass rate, attack success rate, privacy leakage rate, unauthorized tool-use rate, human-approval violation rate, benign-task completion rate, and category-level performance. These measures address different failure modes; a single overall score cannot show whether a policy’s weaknesses involve disclosure, tool authority, approvals, or legitimate task completion.
How to run the benchmark
The project overview specifies Python 3.10 or later. The repository’s quick start uses these commands:
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git clone https://github.com/josepharayemi-netizen/agentguardbench.git
cd agentguardbench
python scripts/generate_dataset.py
PYTHONPATH=src python -m agentguardbench --policy both
python -m unittest discover -s tests -vThe run command evaluates both included policies. The repository says generated results are written to results/ as machine-readable JSON and JSONL. For a new policy, it recommends implementing an adapter that returns an AgentDecision and evaluating it against the same immutable scenario set. Keep evaluation isolated: the repository cautions against using production credentials, real customer data, or tools that can trigger irreversible actions.
Framework coverage is not certification
The repository says AgentGuardBench covers NIST AI 600-1, OWASP Agentic Top 10, OWASP MCP Top 10, and MITRE ATLAS. That is a project description of framework coverage; the cited project pages do not establish independent conformance, certification, or external audit.
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- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
First-release limitations
The project author identifies several constraints that matter when interpreting results:
- Cases are template-driven, and the deterministic reference policies are not a substitute for evaluating stochastic model behavior.
- The translations still need independent review by native speakers.
- The reported baseline does not include repeated stochastic model trials.
- The overview and repository do not publish comparative results for production models.
Planned extensions include adapters for local and hosted models; paraphrased and indirect injections; multi-turn attacks and tool-output poisoning; context-window and memory-contamination tests; cross-tool privilege escalation; held-out challenge sets; native-speaker validation; and independent annotation with uncertainty reporting. These are described as planned work, not capabilities already demonstrated by the reported baseline.
What a benchmark score can—and cannot—tell you
As Arayemi writes in the project overview, “A benchmark score describes a tested configuration. It does not prove that a system is universally safe, ethical, secure or legally compliant.” A result applies to the configuration and scenarios actually tested. For a real deployment, the project says evaluation also needs to account for the model, system prompts, memory, retrieval sources, identity controls, tool implementations, deployment context, users, monitoring, and incident procedures.
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The project presents defensive research, education and training, evaluation-pipeline validation, controlled extension, and authorized security testing as intended uses. Testing should not involve unauthorized targeting, collecting real personal data, bypassing access controls, or automating harmful actions. For details, consult the project overview by Joseph Arayemi and the AgentGuardBench repository.
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