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09/06/2026 11:51 pm
TL;DR: Autonomous red teaming is being positioned as a way to stress-test LLMs and agentic systems before attackers do, while OWASP updates on excessive agency, system prompt leakage, and RAG weaknesses show why traditional red teaming leaves blind spots, according to Lasso Security and OWASP. The real issue is not just model testing, but the lack of clear ownership and benchmarks for systems that now change behaviour, expose prompts, and pull external data at runtime.
NHIMG editorial — based on content published by Lasso Security: Strengthening LLM Security from the Get-Go, autonomous red teaming in action
Questions worth separating out
Q: How should security teams test LLMs that can access tools and external data?
A: Security teams should test LLMs by simulating real runtime abuse, not only prompt injection.
Q: Why do traditional red team exercises miss so many AI security issues?
A: Traditional red team exercises often miss AI security issues because they assume fixed logic, predictable change windows, and static attack surfaces.
Q: When does agentic AI become a governance problem rather than a model-quality problem?
A: Agentic AI becomes a governance problem when the system can select actions, influence downstream workflows, or access data beyond a narrow prompt-response cycle.
Practitioner guidance
- Define model ownership before deployment Assign a named owner for each LLM or agentic workflow, including responsibility for prompts, retrieval sources, tool access, and test cadence.
- Test runtime behaviour, not just output quality Build red-team scenarios that probe prompt leakage, retrieval poisoning, tool misuse, and unsafe autonomous actions.
- Treat prompts and retrieval sources as governed assets Restrict access to system prompts, retrieval indices, and agent instructions with the same care applied to privileged configuration and secrets.
What's in the full article
Lasso Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Model-specific examples of autonomous red teaming workflows across LLM deployments
- The platform’s example outputs for DeepSeek-style security scoring and model card analysis
- How the dashboard presents continuous evaluation metrics for security teams
- The article’s practical framing for combining remediation insights with model testing
👉 Read Lasso Security's analysis of autonomous red teaming for LLM security →
Autonomous red teaming for LLMs: what security teams need now?
Explore further
10/06/2026 2:22 am
Autonomous red teaming is becoming the only credible way to test LLMs that change behavior at runtime. Manual red teaming still matters, but it was built for systems whose boundaries could be enumerated and retested on a schedule. LLMs with dynamic retrieval, prompt-driven behaviour, and tool access alter those boundaries continuously. The implication is that security assurance for AI systems now has to be continuous, not episodic.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: What should organisations do first when securing LLMs and AI agents?
A: Organisations should start by defining ownership, permitted actions, and the boundaries of model access. Before advanced controls, they need to know who is accountable for prompts, retrieval sources, and tool permissions. Without that baseline, testing and monitoring cannot be tied to a clear security decision.
👉 Read our full editorial: Autonomous red teaming exposes the LLM security benchmark gap
