Automated attack emulation can make selected security tests easier to repeat and run regularly, but it cannot prove that an AI system or an organization is secure. The key is to match the tool to the job: AI-specific frameworks help organize attacks against models and AI-enabled systems, while enterprise emulation platforms exercise broader adversary behavior across an environment.
What “red teaming” means in AI security
In conventional cybersecurity, red teaming can mean an authorized group emulating an adversary to assess an organization’s overall security posture. NIST gives this definition, attributed in its report to CNSS (2015): “A group of people authorized and organized to emulate a potential adversary’s attack or exploitation capabilities against an enterprise’s security posture.” NIST distinguishes that broad exercise from penetration testing, which typically focuses on a particular application or system. NIST’s January 2024 taxonomy notes that AI red teaming is often closer to the narrower testing model: evaluators may test a model rapidly or continuously, including under conditions outside normal operation.
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That difference matters when interpreting results. A test of a model’s responses, an attack against an AI agent’s tools, and an exercise against an organization’s network address different risks. Calling each one “red teaming” does not make their scope equivalent.
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What automation changes—and what it does not
Automation is useful when a team has a defined set of attack behaviors it wants to replay, compare, or schedule. A repeatable run can help reveal whether a change to a model, application, or defense alters the outcome of those particular tests. MITRE describes CALDERA as able to execute realistic attack sequences and produce a detailed report. Its paper on AI red teaming recommends recurring exercises across development, deployment, and use. The paper’s recommendation supports building a cadence into security work, not treating a single test as a lasting assurance.
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Repeatability is not completeness. An automated run covers the behaviors and systems configured for it; it does not establish that every relevant threat was considered, that the whole organization was assessed, or that human judgment is unnecessary. Treat the output as evidence about a bounded test, then combine it with threat analysis, expert review, and other security assessments appropriate to the system.
Three tools and frameworks, three different roles
| Resource | Role | Best fit |
|---|---|---|
| MITRE ATLAS | A living knowledge base of tactics and techniques for attacks on AI-enabled systems, informed by observed attacks and realistic demonstrations. | Organizing AI threat coverage and identifying techniques to consider. |
| Arsenal | An automated adversarial attack library implementing ATLAS techniques. MITRE says it was built from Microsoft’s Counterfit. | Emulating selected attacks against systems containing machine learning. |
| MITRE CALDERA | Open-source adversary-emulation software mapped to MITRE ATT&CK, the broader enterprise adversary knowledge base; it can run attack sequences and report findings. | Exercising defined adversary behavior in an enterprise environment. |
These resources are complementary rather than interchangeable. ATLAS helps frame AI-specific techniques; Arsenal provides an attack library for some of those techniques; CALDERA emulates broader enterprise adversary behavior. A team assessing an AI-enabled service may need to consider both the AI-specific attack surface and the surrounding identity, infrastructure, and operational controls.
How to choose and use an emulation approach
Start by defining the claim you want a test to support. “We replayed these selected attacks against this version of the system” is narrower—and more defensible—than “the system is secure.” Then choose an approach based on the system and evidence needed:
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minute- Define scope. Specify the model or application, agent capabilities, connected tools and data, environment, and security question. Decide whether the exercise concerns AI behavior, enterprise infrastructure, or both.
- Map threats to a framework. Use ATLAS to organize AI-specific attack techniques. For broader enterprise adversary emulation, consider the ATT&CK-aligned role CALDERA serves. Framework mappings organize coverage; they do not show that every mapped technique is applicable or tested.
- Select behaviors to emulate. Choose attacks relevant to the system and objective. If an attack library such as Arsenal fits, use it to run selected ATLAS techniques against the machine-learning system. For enterprise sequences, CALDERA can execute defined behaviors and report results.
- Make runs repeatable. Record the target version, configuration, test inputs or sequence, and relevant conditions so a later run can be meaningfully compared. Use recurring tests where the risk and development or deployment cycle justify them.
- Review results in context. Examine what was attempted, what succeeded, what failed, and what the report does and does not cover. Follow up with human analysis and additional testing for risks outside the configured behaviors.
What the agent-hijacking competition shows
In a March 2026 report, NIST’s Center for Advancing Innovation and Standards for Super Intelligence described a competition involving 13 frontier models, more than 400 participants, and over 250,000 attack attempts. It reported at least one successful hijacking attack against each target model. That result is evidence from a particular public competition, focused on hijacking attacks in several agent scenarios. It is not a universal failure rate, a measure of vulnerability across all AI systems, or proof that every model can be compromised in the same way.
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How to interpret the evidence
The cited descriptions establish the different purposes of ATLAS, Arsenal, and CALDERA, and support the value of recurring, defined exercises. They do not provide a direct comparative benchmark for setup effort, expertise required, scheduling features, or effectiveness. Evaluate those factors in the context of your environment rather than assuming that a framework’s existence or a tool’s report demonstrates coverage or security.
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