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Automated attack emulation can make selected security tests easier to repeat, schedule, and report. It does not prove that an AI model, agent, or enterprise is secure: tools exercise defined behaviors, while a full red-team assessment still requires people to choose relevant threats, interpret results, and examine risks beyond the tool’s coverage.
What “red teaming” means in AI security
The term can describe different kinds of work. NIST defines cybersecurity red teaming as “A group of people authorized and organized to emulate a potential adversary’s attack or exploitation capabilities against an enterprise’s security posture.” NIST attributes this definition to CNSS (2015) in its January 2024 report on adversarial machine learning.
A traditional enterprise red-team exercise may combine attack methods to assess an organization’s broader security posture. Penetration testing, by contrast, typically focuses on a particular application or system. In AI practice, “red teaming” is often closer to that narrower testing model: evaluators probe a model under specific conditions, potentially rapidly or continuously and in circumstances outside normal operation. The label alone therefore does not tell you whether an effort assessed an AI model, an agent, an application, or an organization’s full environment.
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Automation can run a chosen set of attack behaviors repeatedly and produce results that teams can review. MITRE describes CALDERA as a platform that executes realistic attack sequences and generates detailed reports. MITRE’s paper on AI red teaming recommends recurring exercises across development, deployment, and use, rather than treating testing as a one-time event.
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That supports a practical advantage: teams can rerun defined tests after a model, system, or configuration changes and compare what happened. It does not mean the same tests cover every relevant threat, that a successful run demonstrates complete security, or that automated tooling replaces human judgment. Results are evidence about the behaviors exercised and the environment tested—not a certificate that everything else is safe.
How ATLAS, Arsenal, and CALDERA differ
These names refer to complementary layers, not interchangeable products. MITRE ATLAS organizes AI-specific adversary tactics and techniques; Arsenal implements ATLAS techniques as an attack library; and CALDERA emulates broader enterprise adversary activity using MITRE ATT&CK.
| Resource | Role | What it helps with |
|---|---|---|
| MITRE ATLAS | Knowledge base of tactics and techniques for attacks on AI-enabled systems, informed by observed attacks and realistic demonstrations. | Organizing and identifying AI-specific threat coverage. |
| Arsenal | Automated adversarial attack library based on Microsoft’s Counterfit and implementing ATLAS techniques. | Emulating selected attacks against systems that contain machine learning. |
| MITRE CALDERA | Open-source adversary-emulation platform mapped to the broader MITRE ATT&CK framework. | Executing enterprise attack sequences and reporting findings. |
ATLAS helps describe what an AI-focused adversary might do; Arsenal provides a way to exercise selected techniques; CALDERA addresses enterprise adversary emulation. Using one does not automatically provide the coverage of the others. In particular, a general ATT&CK-mapped exercise is not, by itself, a test of model-specific or agent-specific attack behavior.
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How to evaluate an automated emulation setup
There is no direct comparative benchmark in the cited sources establishing that one of these resources is best. Evaluate a setup against the system and questions you actually need to test:
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- Scope: Does it target the model or agent, the application around it, the enterprise environment, or some combination?
- Threat alignment: Does the test set map to AI-specific techniques, broader enterprise behaviors, or both?
- Repeatability: Can you rerun the same defined behaviors as the system changes, and can you track what changed between runs?
- Setup and expertise: What configuration, permissions, and security knowledge are needed to run tests safely and interpret them?
- Evidence: What does the output record, and is it detailed enough to support investigation and remediation?
These are evaluation questions, not reported comparative scores. A useful result should make clear which behaviors were attempted, in what scope, and what happened; it should not be presented as evidence for risks that were never tested.
What recent agent testing demonstrates—and what it doesn’t
In a March 2026 NIST report, a competition on hijacking attacks in several agent scenarios involved 13 frontier models, more than 400 participants, and over 250,000 attack attempts. NIST reported at least one successful hijacking attack against each target model.
This is evidence that attacks succeeded in that competition and against those targets. It is not a universal failure rate, a measure of the prevalence of vulnerabilities across all AI systems, or proof that every agent can be hijacked in ordinary use. The result reinforces why agent security needs testing, but its scope should remain attached to the competition and attack class.
How to use automated testing responsibly
For teams asking, “How are you testing your AI agents for security before they hit users?”, a practical approach is to treat emulation as one recurring input to a broader security process:
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- Define the target and scope. Specify whether the exercise covers a model, an agent and its tools, the surrounding application, or enterprise infrastructure.
- Select behaviors deliberately. Use AI-specific techniques for AI and agent risks, and enterprise techniques when the question concerns the broader environment.
- Run in a controlled, authorized environment. Ensure test activity is permitted and does not unintentionally affect users, production systems, or data.
- Review evidence and investigate gaps. Interpret reports in context, follow up on findings, and identify important threats the selected tests did not exercise.
- Repeat at meaningful change points. Revisit the defined tests during development and after relevant deployment or system changes, while recognizing that a recurring test set still has a bounded scope.
The goal is not to automate the red team out of the process. It is to make selected checks more repeatable so people can spend their effort choosing relevant threats, understanding failures, and addressing risks the automation cannot establish.
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