Law / Frameworks / OWASP LLM Top 10

OWASP LLM Top 10 LLM04:2026Supply Chain

LLM supply chains are susceptible to vulnerabilities that affect the integrity of training data, models, adapters, conversion pipelines, and deployment platforms, resulting in biased outputs, security breaches, or system failures. While traditional software vulnerabilities focus on code flaws and dependencies, in ML the risks extend to third-party pre-trained models, datasets, and model artifacts, which can be manipulated through tampering, poisoning, or malicious artifact replacement.OWASP Top 10 for LLM Applications, 2026 edition, August 2026, LLM04:2026

We read each law below as bearing on this control. That does not mean the control, done well, meets the law: what each law asks is on its own page. Corpus as of .

The kinds of duty that reach it: security.

1
law
1
place
0
with court rulings behind them
1
not yet in force

The same ground elsewhere linked through the kinds of duty both controls are mapped from

A law in force is unmarked; the rest wear their state: not yet in force

AI risk obligations

1 law, 1 place
PlaceLawHow it reaches this control
European Union AI Act, Article 15 (accuracy, robustness and cybersecurity) from , in 14 months

Through its security duty. What it requires

Excerpts of the OWASP Top 10 for LLM Applications, CC BY-SA 4.0. OWASP GenAI Security Project, OWASP Top 10 for LLM Applications 2026, https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026/, licensed CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). Excerpted: only the first paragraph of each entry's Description is reproduced, verbatim. Every control of the framework.