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12/06/2026 9:44 pm
TL;DR: The OWASP Top 10 for LLM applications ranks the most common GenAI risks, from prompt injection and insecure output handling to supply chain vulnerabilities and excessive agency, and pairs each with prevention guidance, according to Lasso Security. The list matters because it turns LLM security from an abstract concern into a control checklist that IAM, security, and application teams can operationalise.
NHIMG editorial — based on content published by Lasso Security: OWASP Top 10 LLM Vulnerabilities and Security Checklist
Questions worth separating out
Q: How should security teams implement controls for LLM applications that can access sensitive data?
A: Start by separating model context, retrieval sources, and execution paths so that untrusted text cannot directly influence privileged actions.
Q: Why do LLM applications complicate identity and access management?
A: They complicate IAM because the model can sit between a user, a data source, and an action without being a traditional identity in the human sense.
Q: What do teams get wrong about prompt injection risks?
A: They often treat prompt injection as a content-filtering problem alone.
Practitioner guidance
- Define trust boundaries around model inputs and outputs Separate user content, system instructions, retrieval data, and tool responses so that no single text channel can silently steer privileged behaviour.
- Inventory every LLM dependency and plugin List the APIs, models, extensions, and data feeds that each LLM application can reach, then require review and revocation paths for any dependency that can alter output or access sensitive context.
- Gate state-changing actions behind policy checks Require human approval or policy-engine approval before any LLM output can change permissions, send money, expose records, or trigger automation with meaningful business impact.
What's in the full article
Lasso Security's full guide covers the operational detail this post intentionally leaves for the source:
- Item-by-item breakdown of all ten OWASP LLM risks with applied examples.
- Prevention guidance for prompt injection, output sanitisation, and model denial of service.
- Checklist language for teams building LLM app security controls and review processes.
- Plain-English mitigation notes for supply chain, plugin, and model theft scenarios.
👉 Read Lasso Security's guide to the OWASP Top 10 LLM vulnerabilities →
OWASP LLM Top 10: are your GenAI controls keeping up?
Explore further
13/06/2026 12:00 am
OWASP’s LLM Top 10 is really an identity boundary document, not just an application checklist. The article’s risks all converge on one issue: who or what is trusted to influence model behaviour, output handling, and downstream action. Once an LLM can ingest external content, call tools, or shape privileged workflows, the security problem becomes identity, not just prompt hygiene. Practitioners should treat LLM risk registers as control-boundary design work.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Organisations maintain an average of 6 distinct secrets manager instances, creating fragmentation that undermines centralised control, according to The State of Secrets in AppSec.
A question worth separating out:
Q: Who should be accountable when an LLM triggers an unauthorized action?
A: Accountability should rest with the team that granted the LLM its reach, defined its permissions, and allowed the action to proceed without adequate gating. The right governance model treats the model as a system component with delegated authority, not as a black box that sits outside ownership.
👉 Read our full editorial: OWASP LLM Top 10 clarifies the security gaps in GenAI apps
