Topic starter
05/07/2026 6:51 pm
TL;DR: The GenAI security landscape is being mapped to the OWASP Top 10 for LLM Applications (2025), highlighting runtime and development-stage coverage for prompt injection, sensitive data disclosure, excessive agency, and output-handling risks, according to Lakera. The deeper lesson is that AI security now spans lifecycle governance, not just model filtering.
Editorial analysis by NHI Mgmt Group, based on content published by Lakera: “Aligning with the OWASP Top 10 for LLMs (2025): How Lakera Secures GenAI Applications”.
Key questions
Q: What breaks when LLMs are protected only with runtime filters?
A: Runtime filters can reduce harmful prompts and outputs, but they do not solve upstream poisoning, dependency compromise, or over-scoped tool access.
Q: Why do LLMs create risk in identity and access management?
A: LLMs create risk when teams confuse fluent answers with verified security evidence.
Q: How do organisations decide when an LLM is safe enough for production use?
A: They should evaluate the exact workflow, not the model in isolation.
Practitioner guidance
- Map LLM controls by lifecycle stage Separate development-time testing, runtime filtering, and supply-chain assurance into distinct control owners and success criteria so coverage gaps are visible.
- Restrict tool-facing permissions Treat every connected API, workflow, or data source as a privileged boundary and grant only the minimum action scope required for the task.
- Test for prompt injection and disclosure paths Run adversarial evaluations against user prompts, retrieved content, and system instructions to see what the model can be made to reveal or do.
Bottom line: LLM risk now spans the full lifecycle, from adversarial testing to live enforcement and upstream trust controls.
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05/07/2026 7:11 pm
LLM security has moved from prompt filtering to lifecycle governance. The 2025 OWASP map makes one thing clear: the control problem begins before inference and continues after output. That means development-time red teaming, runtime protection, and upstream integrity checks are different layers of the same governance model, not interchangeable features. Practitioners should stop treating LLM security as a single control plane.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- Generative AI use specifically increased from 33% in 2023 to 79% in 2025, according to McKinsey’s Global Surveys on the State of AI.
A question worth separating out:
Q: What is the difference between model behaviour testing and runtime protection for LLMs?
A: Model behaviour testing looks for weaknesses before deployment, such as injection sensitivity or disclosure tendencies. Runtime protection inspects live traffic and output to stop harmful behaviour as it happens. The first finds latent risk, while the second limits impact after the system is already in use.
👉 Read our full editorial: OWASP Top 10 for LLMs: what Lakera’s coverage changes