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14/09/2026 10:10 am
TL;DR: Prompt injection, system prompt leakage, unbounded consumption, poisoning, excessive agency, vector weaknesses, and insecure framework configuration remain the core failure modes in GenAI development, according to Flatt Security’s analysis of OWASP Top 10 for LLM Applications 2025. The practical takeaway is that LLM security is a control-design problem, not a model-quality problem, and the identity boundary matters whenever prompts, tools, credentials, or RAG contexts are involved.
NHIMG editorial — based on content published by Flatt Security: LLM App Security risk and prevention for GenAI development
Questions worth separating out
Q: How should security teams handle prompt injection in production LLM applications?
A: Security teams should treat prompt injection as a runtime control issue, not a content-moderation problem.
Q: When does an LLM become a privilege problem rather than a chatbot?
A: An LLM becomes a privilege problem the moment it can read, write, call APIs, or act on behalf of users.
Q: What do teams get wrong about system prompt leakage?
A: Teams often assume hidden prompts are protected because users cannot see them directly, but any text the application exposes to the model can potentially be recovered through crafted interactions.
Practitioner guidance
- Constrain tool permissions to task scope Give each LLM workflow only the permissions needed for the specific action it must perform.
- Separate prompts from security controls Do not store API keys, tokens, or authorization rules in system prompts.
- Treat retrieved content as untrusted Apply validation and filtering to documents, web pages, files, and other data that may enter RAG or tool-linked workflows.
What's in the full article
Flatt Security's full article covers the implementation detail this post intentionally leaves at the analysis level:
- Worked examples showing how direct and indirect prompt injection alter LLM behaviour in real application flows
- Code-level demonstrations of system prompt leakage, including how sensitive values can be exposed through crafted prompts
- Framework-specific configuration issues such as unsafe template handling and overly broad file-system access
- Practical mitigation notes tied to OWASP Top 10 for LLM Applications 2025 categories
👉 Read Flatt Security's analysis of LLM app security risks for GenAI development →
LLM app security risks: where GenAI controls break down?
Explore further
14/09/2026 10:11 am
LLM application security is really identity and authority design in disguise. Once a model can act through tools, storage, or APIs, the real question becomes who or what is authorised to perform those actions. That means GenAI security cannot be separated from IAM, secrets management, and privilege scoping. The practitioner implication is clear: model risk reviews must include identity, not just prompt safety.
A question worth separating out:
Q: How can organisations tell if an LLM framework is configured too permissively?
A: Look for broad file-system access, unsafe template evaluation, write permissions that are not required, and helper functions that can execute beyond the intended workflow. If the framework can touch data or systems the task does not need, the configuration is too broad and should be reduced.
👉 Read our full editorial: LLM app security risks show where GenAI controls fail
