AI Agent Safety Benchmark Comparison
There is no single agent safety score. Prompt injection resistance, risky tool use, harmful-goal refusal, and cyber capability are different questions. This comparison shows what four widely used benchmark families measure and where each result can mislead.
Start with the practical AI agent safety guide →Quick Comparison
| Benchmark | Primary safety question | Evaluation setting | Main limitation |
|---|---|---|---|
| AgentDojo | Will an agent follow indirect prompt injection? | Tool-using tasks with benign goals and injected content | Covers a defined set of applications and attacks |
| ToolEmu | Will an agent take risky actions with tools? | Emulated tools and adversarial failure scenarios | Simulation may differ from production integrations |
| AgentHarm | Will an agent refuse or complete harmful goals? | Multi-step malicious tasks across harm categories | Focuses on explicitly harmful requests |
| CyberSecEval | What cyber capabilities and misuse risks exist? | Cybersecurity tasks, including agentic scenarios | Domain-specific rather than general agent safety |
Do not rank the four rows with one number. A system can resist prompt injection and still take unsafe actions after a normal planning error. It can refuse harmful user requests and still expose secrets through a compromised tool result.
AgentDojo: Prompt Injection Resistance
AgentDojo evaluates tool-using agents on useful tasks while malicious instructions are placed inside data the agent reads, such as messages or documents. The central tension is whether the agent completes the user's task without obeying an instruction from an untrusted source.
Use it when your agent reads email, web pages, files, or other content that an attacker can influence. Track benign task success and attack success separately; a defense that blocks every tool call may look safe while making the agent useless.
ToolEmu: Risky Tool-Use Behavior
ToolEmu uses emulated tools and adversarial scenarios to probe whether an agent notices missing information, asks for confirmation, and avoids actions with unacceptable side effects. It is useful for failures that do not require a malicious user, such as deleting the wrong file or sending private data to the wrong destination.
Map its scenarios to your real permissions. An emulated payment or file tool can reveal planning weaknesses, but production safety also depends on authentication, scopes, rate limits, and reversible operations.
AgentHarm: Harmful Multi-Step Goals
AgentHarm tests whether an agent can and will complete explicitly harmful tasks that require several actions. It goes beyond a text-only refusal because the system must decide whether to invoke tools and continue after intermediate results.
Use it to evaluate refusal boundaries and the gap between what a model says and what an agent actually does. It does not replace benign-task testing, prompt injection evaluation, or domain-specific compliance.
CyberSecEval: Cyber Capability and Misuse
CyberSecEval covers cybersecurity risks and capabilities, with suites that can include code, vulnerability, and autonomous cyber behavior. It is relevant when an agent can execute code, operate a browser, scan systems, or access development infrastructure.
Treat cyber results as a specialist layer. A customer-support agent may need strong prompt injection and data-exfiltration tests even if it has no reason to run a full autonomous cyber benchmark.
Build an Evaluation Portfolio
- Start with real benign tasks and measure completion, latency, and unnecessary tool calls.
- Add indirect prompt injection whenever the agent consumes untrusted content.
- Add risky-action scenarios for every tool with external side effects.
- Test harmful-goal refusal for the domains and permissions you expose.
- Add cyber evaluations when code execution or system access is in scope.
Pair public benchmarks with private cases based on your tools, policies, and data. Public suites make model comparisons repeatable; private evaluations reveal whether your actual product boundaries work.
How to Compare Results
Keep task success, attack success, harmful completion, and unnecessary refusal as separate metrics. Report the benchmark version, model, prompt, tool implementation, permission settings, and number of trials. Agent results can change when any one of those inputs changes.
Evaluate confidence intervals or repeat variability when actions are sampled. A small percentage-point difference from one run is not a reliable ranking. Most importantly, test defenses against normal tasks: safety improvements that destroy task completion are not free.
Frequently Asked Questions
Which AI agent safety benchmark is best?
There is no universal best benchmark. AgentDojo is useful for indirect prompt injection, ToolEmu for risky actions, AgentHarm for harmful goals, and CyberSecEval for cyber-specific capability and misuse.
Can benchmark scores prove an agent is safe?
No. A benchmark samples known tasks and attacks. Production safety also depends on permissions, tool design, user data, monitoring, incident response, and threats not represented in the suite.
Should safety and capability use the same test set?
Keep separate metrics but run both. You need to know whether a defense blocks attacks and whether it still allows legitimate tasks to finish.
Next Steps
Apply the comparison to the controls in the AI agent safety guide, and review how tools and feedback enter the agent loop.
Turn benchmark findings into safeguards →Return to the complete architecture guide →ai agent safety benchmark comparison — return to the complete AI agent architecture guide.
Was this helpful?
Your feedback stays on this page — no tracking.