AIUC-1 × MITRE ATLAS - AIUC-1

MITRE ATLAS is a knowledge base of adversarial tactics, techniques and mitigation strategies for machine learning systems, similar to MITRE ATT&CK for cybersecurity.

AIUC-1 integrates MITRE ATLAS, which is a technical contributor to AIUC-1. Certification against AIUC-1:

Incorporates ATLAS mitigation strategies in requirements and controls
Strengthens robustness against the adversarial tactics and techniques identified in ATLAS
Goes beyond ATLAS’s focus on security alone

MITRE ATLAS crosswalks by mitigation strategy

ATLAS mitigation strategy	ATLAS description	Relevant AIUC-1 requirements
AML-M0000: Limit Public Release of Information	Limit the public release of technical information about the AI stack used in an organization’s products or services. Technical knowledge of how AI is used can be leveraged by adversaries to perform targeting and tailor attacks to the target system. Additionally, consider limiting the release of organizational information - including physical locations, researcher names, and department structures - from which technical details such as AI techniques, model architectures, or datasets may be inferred.	B003: Manage public release of technical details
AML-M0001: Limit Model Artifact Release	Limit public release of technical project details including data, algorithms, model architectures, and model checkpoints that are used in production, or that are representative of those used in production.	B003: Manage public release of technical details
AML-M0002: Passive AI Output Obfuscation	Decreasing the fidelity of model outputs provided to the end user can reduce an adversaries ability to extract information about the model and optimize attacks for the model.	B009: Limit output over-exposure
AML-M0003: Model Hardening	Use techniques to make AI models robust to adversarial inputs such as adversarial training or network distillation.	B001: Third-party testing of adversarial robustness B002: Detect adversarial input B004: Prevent AI endpoint scraping
AML-M0004: Restrict Number of AI Model Queries	Limit the total number and rate of queries a user can perform.	D003: Restrict unsafe tool calls B001: Third-party testing of adversarial robustness B004: Prevent AI endpoint scraping
AML-M0005: Control Access to AI Models and Data at Rest	Establish access controls on internal model registries and limit internal access to production models. Limit access to training data only to approved users.	B008: Protect AI system deployment environment B007: Enforce user access privileges to AI systems
AML-M0006: Use Ensemble Methods	Use an ensemble of models for inference to increase robustness to adversarial inputs. Some attacks may effectively evade one model or model family but be ineffective against others.
AML-M0007: Sanitize Training Data	Detect and remove or remediate poisoned training data. Training data should be sanitized prior to model training and recurrently for an active learning model. Implement a filter to limit ingested training data. Establish a content policy that would remove unwanted content such as certain explicit or offensive language from being used.
AML-M0008: Validate AI Model	Validate that AI models perform as intended by testing for backdoor triggers or adversarial bias. Monitor model for concept drift and training data drift, which may indicate data tampering and poisoning.
AML-M0009: Use Multi-Modal Sensors	Incorporate multiple sensors to integrate varying perspectives and modalities to avoid a single point of failure susceptible to physical attacks.
AML-M0010: Input Restoration	Preprocess all inference data to nullify or reverse potential adversarial perturbations.
AML-M0011: Restrict Library Loading	Prevent abuse of library loading mechanisms in the operating system and software to load untrusted code by configuring appropriate library loading mechanisms and investigating potential vulnerable software. File formats such as pickle files that are commonly used to store AI models can contain exploits that allow for loading of malicious libraries.
AML-M0012: Encrypt Sensitive Information	Encrypt sensitive data such as AI models to protect against adversaries attempting to access sensitive data.	B008: Protect AI system deployment environment
AML-M0013: Code Signing	Enforce binary and application integrity with digital signature verification to prevent untrusted code from executing. Adversaries can embed malicious code in AI software or models. Enforcement of code signing can prevent the compromise of the AI supply chain and prevent execution of malicious code.	E004: Assign accountability
AML-M0014: Verify AI Artifacts	Verify the cryptographic checksum of all AI artifacts to verify that the file was not modified by an attacker.
AML-M0015: Adversarial Input Detection	Detect and block adversarial inputs or atypical queries that deviate from known benign behavior, exhibit behavior patterns observed in previous attacks or that come from potentially malicious IPs. Incorporate adversarial detection algorithms into the AI system prior to the AI model.	B002: Detect adversarial input B005: Implement real-time input filtering
AML-M0016: Vulnerability Scanning	Vulnerability scanning is used to find potentially exploitable software vulnerabilities to remediate them. File formats such as pickle files that are commonly used to store AI models can contain exploits that allow for arbitrary code execution. These files should be scanned for potentially unsafe calls, which could be used to execute code, create new processes, or establish networking capabilities. Adversaries may embed malicious code in model corrupt model files, so scanners should be capable of working with models that cannot be fully de-serialized. Both model artifacts and downstream products produced by models should be scanned for known vulnerabilities.	C002: Conduct pre-deployment testing
AML-M0017: AI Model Distribution Methods	Deploying AI models to edge devices can increase the attack surface of the system. Consider serving models in the cloud to reduce the level of access the adversary has to the model. Also consider computing features in the cloud to prevent gray-box attacks, where an adversary has access to the model preprocessing methods.	E005: Document data storage security
AML-M0018: User Training	Educate AI model developers on secure coding practices and AI vulnerabilities.
AML-M0019: Control Access to AI Models and Data in Production	Require users to verify their identities before accessing a production model. Require authentication for API endpoints and monitor production model queries to ensure compliance with usage policies and to prevent model misuse.	B008: Protect AI system deployment environment B007: Enforce user access privileges to AI systems
AML-M0020: Generative AI Guardrails	Guardrails are safety controls that are placed between a generative AI model and the output shared with the user to prevent undesired inputs and outputs. Guardrails can take the form of validators such as filters, rule-based logic, or regular expressions, as well as AI-based approaches, such as classifiers and utilizing LLMs, or named entity recognition (NER) to evaluate the safety of the prompt or response. Domain specific methods can be employed to reduce risks in a variety of areas such as etiquette, brand damage, jailbreaking, false information, code exploits, SQL injections, and data leakage.	A004: Protect IP & trade secrets A006: Prevent PII leakage A007: Prevent IP violations C006: Prevent output vulnerabilities C007: Flag high risk outputs for human review
AML-M0021: Generative AI Guidelines	Guidelines are safety controls that are placed between user-provided input and a generative AI model to help direct the model to produce desired outputs and prevent undesired outputs. Guidelines can be implemented as instructions appended to all user prompts or as part of the instructions in the system prompt. They can define the goal(s), role, and voice of the system, as well as outline safety and security parameters.	B002: Detect adversarial input B005: Implement real-time input filtering
AML-M0022: Generative AI Model Alignment	When training or fine-tuning a generative AI model it is important to utilize techniques that improve model alignment with safety, security, and content policies. The fine-tuning process can potentially remove built-in safety mechanisms in a generative AI model, but utilizing techniques such as Supervised Fine-Tuning, Reinforcement Learning from Human Feedback or AI Feedback, and Targeted Safety Context Distillation can improve the safety and alignment of the model.
AML-M0023: AI Bill of Materials	An AI Bill of Materials (AI BOM) contains a full listing of artifacts and resources that were used in building the AI. The AI BOM can help mitigate supply chain risks and enable rapid response to reported vulnerabilities. This can include maintaining dataset provenance, i.e. a detailed history of datasets used for AI applications. The history can include information about the dataset source as well as well as a complete record of any modifications.	E017: Document system transparency policy
AML-M0024: AI Telemetry Logging	Implement logging of inputs and outputs of deployed AI models. Monitoring logs can help to detect security threats and mitigate impacts. Additionally, having logging enabled can discourage adversaries who want to remain undetected from utilizing AI resources.	E009: Monitor third-party access E015: Log AI system activity D003: Restrict unsafe tool calls B002: Detect adversarial input
AML-M0025: Maintain AI Dataset Provenance	Maintain a detailed history of datasets used for AI applications. The history should include information about the dataset’s source as well as a complete record of any modifications.	E017: Document system transparency policy

Last updated July 22, 2025.