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GenAI Security: The OWASP Top 10 for LLM Applications

The 2026 OWASP GenAI LLM Top 10 is grounded in ~10,000 real incidents. Learn the actual risks — prompt injection, excessive agency, misinformation — and the concrete mitigations for devs.

Published on • August 8, 2026

AI Assistant

The 2026 OWASP GenAI LLM Top 10 changed how the field ranks risk. Previous editions leaned on expert voting; the 2026 edition (published August 4, 2026) backs its rankings with roughly 10,000 real-world AI security incidents. The result shifts priorities — and the message it leads with is blunt: “Stop trying to build a model that cannot be fooled. Build the system around it, so that when the model is fooled, nothing important breaks.”

LLM01 · Prompt injection

Direct input and retrieved content can hijack the model: it cannot reliably tell data from instructions. There’s no SQL-injection-style fix — treat it as an ongoing operational risk.

  • Treat all retrieved context as untrusted data — never concatenate it into a privileged instruction verbatim.
  • Separate instructions from content with clear delimiters and a strict output path.
  • Least privilege for tools: a document reader should only read the doc it’s asked about.
  • Allowlist model actions; monitor for anomaly.

LLM02 · Sensitive information disclosure

Models leak data via legacy authorization, leftover credentials, or training data regurgitation. The incident record keeps it #2 — and cross-modal attacks (hidden audio/image instructions) now land here too.

  • Keep secrets out of prompts and context, period.
  • Apply per-user authorization to retrieved content, not just the output.
  • Redact and re-check outputs for PII patterns before they reach storage.

LLM03 · Excessive agency (climbing fast)

Gave an agent “read the docs” and the tool you bound also wrote and deleted them? That’s excessive agency — 8th→3rd in a two-year arc, and it maps directly to token budgets, tool-call caps, and per-tool permission boundaries.

  • Scope every tool: no tool gets more capability than its use case requires.
  • Require human approval for destructive/expensive actions.
  • Timebox sessions and cap the total number of tool calls.
  • Continuously monitor behavior for drift from the intended operations.

LLM04 · Unbounded consumption

Bouncy resource and cost exhaustion climbed four places — practitioners now weight runaway loops more heavily. Unbounded agent loops are a documented billing vulnerability (a single uncapped CrewAI run was clocked at $414).

  • Set hard per-request and per-session token budgets.
  • Enforce provider-level retry/rate limits and idempotent tool calls.
  • Dashboards at cost per session, not just per request.

LLM05 · System prompt leakage / Hidden context exposure

Inline secrets, chain-of-thought, or internal system prompts can leak to a user or a downstream actor. Hidden context also gets exposed via caching and chain-of-thought reasoning.

  • Move secrets to a separate secrets manager or server-side.
  • Cache prompted data with the authorization attached — the cache key must not leak visible/organizational metadata.
  • Don’t echo model reasoning to end users.

LLM06 · Misinformation (climbed)

Incorrect/incomplete/misleading output is now treated as a system-level failure: model outputs drive tool calls, authorize actions, coordinate agents.

  • Ground outputs in evidence; use citations + verification.
  • Eval suites that assert the model doesn’t fabricate (refusal rate).
  • Human review for the low-confidence band.

LLM07 · Insecure output handling

Model output that goes straight into SQL, HTML, or commands becomes injection again. It dropped to #10 (absorbed into other categories) but is not gone.

  • Treat model output like untrusted input: parameterize, escape, validate.
  • Never let a model dictate formatted queries without bounds.

LLM08 · Supply chain, data & model poisoning

Fine-tuned weights, RAG document corpora, and plugin dependencies are all poisoning vectors — and the category now explicitly includes fine-tuning subversion.

  • Pin and hash weights/datasets/plugins.
  • Verify provenance (provenance tools on the Hub, SBOMs).
  • Safety-test any fine-tune you didn’t train yourself; review data pipelines.

LLM09 · Insecure plugins & tools

MCP servers, browser tools, code exec — each a supply chain + capability boundary.

  • Set minimum permissions on every MCP tool you mount.
  • Sandbox execution (Wasm, gVisor).
  • Audit tool registries like a dependency tree.

LLM10 · Model theft

Not unique and easily prevented: restrict endpoint access, apply privacy/security guardrails, and certify model boundaries. The cheapest and most boring entry — do it.

Putting It All Together: a dev checklist

  1. Enumerate every model input channel: user text, retrieved content, fed files, images.
  2. Trust nothing: treat all body of context as attacker-controlled; token and schema-check outputs.
  3. Give each tool the minimum capability and apply permission/approval for anything irreversible.
  4. Put hard token caps per request, per session, and per project (self-server) before the loop runs.
  5. Add “does it refuse / does it fabricate” tests to your eval harness.

Conclusion & Next Steps

The 2026 Top 10’s real lesson is blast-radius control: the model will be fooled, so the defense is the perimeter around it — tool scoping, budget caps, output validation, guardrails. Next: walk your own system against this 10-point list, then threat-model the agentic tier (tools, memory, consequences) against the separate OWASP Agentic Top 10, where today’s agent failures actually land.

References / Sources

ai security owasp llms genai