AIUC-1
C006

Prevent output vulnerabilities

Implement safeguards to prevent security vulnerabilities in outputs from impacting users

Keywords
Harmful Outputs
Code Injection
Data Exfiltration
Application
Mandatory
Frequency
Every 3 months
Type
Preventative
Crosswalks
AML-M0020: Generative AI Guardrails
Article 72: Post-Market Monitoring by Providers and Post-Market Monitoring Plan for High-Risk AI Systems
LLM05:25 - Improper Output Handling

Control activities

Establishing output sanitization and validation procedures before presenting content to users. For example, stripping or encoding HTML, JavaScript, shell syntax, and iframe content, blocking or rewriting unsafe URLs, validating structured output schemas (e.g. JSON/YAML/XML) against whitelists, enforcing safe rendering modes (e.g. text-only, content-security-policy (CSP) headers).

Implementing safety-specific labeling and handling protocols. For example, clearly marking untrusted, distinguishing untrusted third-party data, applying appropriate security controls based on content source and risk level.

Maintaining detection and monitoring capabilities. For example, logging sanitization activities, implementing alerting for suspicious content patterns.

Detecting advanced output-based attack patterns. For example, identifying prompt injection chains, model-output subversion (e.g. jailbreak tokens), payloads targeting downstream applications (e.g. command-line instructions, SQL queries), or obfuscated exploits designed to bypass basic filters.

Organizations can submit alternative evidence demonstrating how they meet the requirement.

AIUC-1 is built with industry leaders

Phil Venables

"We need a SOC 2 for AI agents— a familiar, actionable standard for security and trust."

Google Cloud
Phil Venables
Former CISO of Google Cloud
Dr. Christina Liaghati

"Integrating MITRE ATLAS ensures AI security risk management tools are informed by the latest AI threat patterns and leverage state of the art defensive strategies."

MITRE
Dr. Christina Liaghati
MITRE ATLAS lead
Hyrum Anderson

"Today, enterprises can't reliably assess the security of their AI vendors— we need a standard to address this gap."

Cisco
Hyrum Anderson
Senior Director, Security & AI
Prof. Sanmi Koyejo

"Built on the latest advances in AI research, AIUC-1 empowers organizations to identify, assess, and mitigate AI risks with confidence."

Stanford
Prof. Sanmi Koyejo
Lead for Stanford Trustworthy AI Research
John Bautista

"AIUC-1 standardizes how AI is adopted. That's powerful."

Orrick
John Bautista
Partner at Orrick and creator of the YC SAFE
Lena Smart

"An AIUC-1 certificate enables me to sign contracts must faster— it's a clear signal I can trust."

SecurityPal
Lena Smart
Head of Trust for SecurityPal and former CISO of MongoDB
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