Notifications
Clear all
Topic starter
11/08/2026 3:13 pm
TL;DR: AI Security Validation has been mapped to the OWASP Top 10 for LLM Applications, with initial scenarios covering malicious general actions, malicious cyber actions, exfiltration, and system prompt leakage across AWS Bedrock first and Azure OpenAI next, according to SafeBreach. The practical shift is that LLM risk is now measurable against real guardrails, and agentic systems will only widen the blast radius.
NHIMG editorial — based on content published by SafeBreach: Coverage for the OWASP Top 10 for LLMs
Questions worth separating out
Q: How should security teams test LLM guardrails across multiple languages?
A: Security teams should test the same harmful intent in every major language and in code-switched variants, then compare block, warn, and allow outcomes.
Q: Why do connected LLMs create new access-control risk?
A: Because the model sits between users and sensitive data, it can become a disclosure path even when no infrastructure component is breached.
Q: What do security teams get wrong about prompt injection defence?
A: They often assume better blocklists will solve the problem, but obfuscation simply changes the shape of the payload.
Practitioner guidance
- Validate prompt refusal paths Run repeatable tests against system prompts, guardrails, and retrieval prompts to see whether malicious instructions are blocked or followed under realistic abuse conditions.
- Constrain retrieval to least privilege Limit which documents, records, and knowledge sources a model can access, then review whether output filtering prevents sensitive information disclosure when those sources are queried.
- Treat agent permissions as NHI permissions If an LLM can call tools or actions, assign narrow, task-specific access and review it like a non-human identity with explicit expiry and revocation rules.
What's in the full article
SafeBreach's full coverage covers the operational detail this post intentionally leaves for the source:
- Attack scenario descriptions for malicious general actions, malicious cyber actions, exfiltration, and system prompt leakage
- Coverage specifics for AWS Bedrock first and Azure OpenAI as the follow-on platform
- Blocked or not blocked result handling and how to interpret each test outcome
- The relationship between the Hacker's Playbook and AI Security Validation in the CTEM workflow
👉 Read SafeBreach's coverage of OWASP Top 10 for LLM validation →
OWASP LLM risks in production: are your controls keeping up?
Explore further
11/08/2026 3:27 pm
LLM security validation is becoming a control discipline, not a product feature. The article shows why reading the OWASP taxonomy is not enough when models are already in production. Security teams need empirical evidence about what the model will do under attack, because guardrails, retrieval scope, and output policies fail in different ways depending on deployment. The practical conclusion is that AI assurance belongs in the testing program alongside other control validation.
A question worth separating out:
Q: How should organisations govern external tools used by AI agents?
A: Organisations should review external tools as security inputs, not convenience features. Each tool needs ownership, approval, metadata inspection, and ongoing monitoring for hidden instructions or unexpected behaviour. If an AI agent can act on a tool, then the tool’s provenance and control status should be governed like any other sensitive integration.
👉 Read our full editorial: LLM security validation is shifting from theory to live testing
