By Sapumal Herath · Owner & Blogger, AI Buzz · Last updated: February 2, 2026 · Difficulty: Beginner
Most teams secure their GenAI apps like they’re “just another web feature.”
But LLM apps introduce new failure modes that don’t fit classic AppSec checklists: prompt injection, data leaks through chat logs, unsafe outputs that get executed downstream, and agent workflows that can take real actions.
That’s why the OWASP Top 10 Risks & Mitigations for LLMs and GenAI Apps (2025) is so useful. It gives teams a shared language for the most common risks—and a practical starting point for security, governance, monitoring, and incident response.
Note: This article is for educational and defensive purposes only. It does not provide instructions for wrongdoing. Always follow your organization’s policies and applicable laws.
🎯 What this OWASP “Top 10” is (plain English)
This OWASP list is a security-focused “top risks” guide for building and operating LLM and GenAI applications.
It’s not saying “every app will be hacked.” It’s saying: these are the most common, most costly ways GenAI apps fail—especially once you add RAG, plugins/connectors, and agentic tool use.
If you’re deploying any of these, this list applies to you:
- Chatbots and copilots (internal or customer-facing)
- RAG apps (answering from your docs/KB/wiki)
- Tool-connected agents (email, tickets, repos, databases)
- GenAI embedded in SaaS workflows (drafting, classification, automation)
🧠 The 10 risks (2025) at a glance
Here is the OWASP 2025 Top 10 list in plain English, with the “what it usually looks like” summary:
| Risk | What it means (plain English) | What it looks like in real life |
|---|---|---|
| LLM01: Prompt Injection | Untrusted instructions steer the model’s behavior | Model follows hidden instructions in a doc/webpage and does the wrong thing |
| LLM02: Sensitive Information Disclosure | Private data leaks via outputs, prompts, logs, or retrieval | Customer data or secrets appear in responses or admin logs |
| LLM03: Supply Chain | Risk from third-party models, datasets, tools, dependencies | Compromised model, vulnerable plugin, unclear vendor terms, poisoned packages |
| LLM04: Data & Model Poisoning | Training, fine-tuning, or knowledge sources get corrupted | Model “learns” attacker-preferred behavior or RAG content gets manipulated |
| LLM05: Improper Output Handling | Downstream systems trust model output without validation | AI output becomes HTML/code/commands that execute or trigger unsafe actions |
| LLM06: Excessive Agency | AI has too much autonomy to take actions | Agent can send, delete, merge, publish, change records without approval |
| LLM07: System Prompt Leakage | Hidden instructions leak (and reveal how to bypass controls) | Users extract internal prompts, policies, or sensitive operational details |
| LLM08: Vector & Embedding Weaknesses | RAG/vector stores become a new attack surface | Poisoned content gets retrieved; access boundaries fail; retrieval can leak data |
| LLM09: Misinformation | Credible-sounding wrong answers (hallucinations) | Fake policies, invented citations, wrong instructions—trusted by users |
| LLM10: Unbounded Consumption | Runaway usage causes outages or “denial-of-wallet” costs | Token spikes, tool loops, expensive prompts, abusive traffic |
Tip: If you only have bandwidth to fix a few things first, focus on: Prompt Injection, Sensitive Data Disclosure, Improper Output Handling, and Excessive Agency.
🧨 LLM01: Prompt Injection (what to do)
What it is: the model is manipulated by instructions that should not be trusted.
Where it shows up: web browsing, email/PDF summarization, RAG retrieval, ticket analysis, and any agent that reads untrusted content.
Defensive guardrails:
- Separate trusted instructions from untrusted content (don’t mix “system rules” with raw documents).
- Least privilege for tools and data (read-only by default).
- Approval gates for high-impact actions (send/publish/delete/merge/payment).
- Adversarial testing with “tricky content” (real docs/webpages) before scaling.
AI Buzz companion read: Prompt Injection Explained (Defensive Guide)
🔐 LLM02: Sensitive Information Disclosure (what to do)
What it is: sensitive information leaks—through the model output, through logs, through retrieval, or through user prompt habits.
Defensive guardrails:
- Define “Red data” that must never go into AI tools (passwords, API keys, secrets, regulated data unless explicitly approved).
- Redaction/DLP mindset for prompts, outputs, and logs.
- Permission-aware retrieval (RAG must respect RBAC/tenant boundaries).
- Retention control (don’t let logs become a permanent secrets database).
🧩 LLM03: Supply Chain (what to do)
What it is: your GenAI app depends on third-party components: model providers, open models, LoRA adapters, embeddings tools, plugins/connectors, and data sources. If any are compromised—or simply poorly governed—you inherit the risk.
Defensive guardrails:
- Vendor due diligence (retention, training usage, audit logs, incident notifications).
- Inventory everything (models, connectors, datasets, RAG sources) and track versions.
- Prefer verifiable sources and integrity checks for externally sourced components.
AI Buzz companion reads:
🧬 LLM04: Data & Model Poisoning (what to do)
What it is: attackers (or accidents) introduce bad data into training, fine-tuning, feedback loops, or RAG knowledge sources—leading to harmful or incorrect behavior.
Defensive guardrails:
- Secure your data pipeline (who can add/edit data and when).
- Provenance + review gates before content becomes “trusted.”
- Evaluation sets that detect regressions after updates.
- Monitoring for drift and retrieval quality (poisoning often looks like “quality got worse”).
AI Buzz companion read: Datasheets for Datasets
⚠️ LLM05: Improper Output Handling (what to do)
What it is: the most underrated risk. It happens when your system treats LLM output as “safe” and passes it into code, HTML, templates, database queries, automation rules, or security decisions without validation.
Defensive guardrails:
- Never execute LLM output directly without strict validation.
- Use structured outputs (schemas) for tool calls and automation steps.
- Validate and sanitize outputs before downstream use (same mindset as input validation in AppSec).
- Keep high-impact actions draft-only until a human approves.
🧰 LLM06: Excessive Agency (what to do)
What it is: giving the AI system too much power to act.
Defensive guardrails:
- Least privilege (read-only by default).
- Approval gates for write actions and irreversible actions.
- Step limits and budget limits to prevent runaway chains.
- Audit logs for every tool call (who, what, when, why).
AI Buzz companion read: AI Incident Response Playbook
🪟 LLM07: System Prompt Leakage (what to do)
What it is: your hidden system instructions leak to users. This can reveal internal rules, guardrails, operational details, and sometimes even sensitive configuration hints.
Defensive guardrails:
- Never store secrets in prompts (no credentials, tokens, internal-only secrets).
- Design guardrails as system controls, not “prompt-only promises.”
- Monitor for prompt extraction attempts (probing behavior).
🧠 LLM08: Vector & Embedding Weaknesses (RAG security)
What it is: RAG systems add a “memory layer” (vectors/embeddings). That memory can be attacked: poisoned, mis-scoped, or retrieved incorrectly—leading to leaks or manipulated outputs.
Defensive guardrails:
- Access control around vector stores (tenant boundaries and permission-aware retrieval).
- Content validation before indexing (reduce poisoning risk).
- Retrieval monitoring: relevance, stale sources, empty retrieval, suspicious spikes.
- Immutable logs of what was retrieved and why (so you can investigate incidents).
📰 LLM09: Misinformation (hallucinations) — why it’s a security issue
What it is: false or misleading content that looks credible.
Why it matters: misinformation becomes a security problem when it drives actions: wrong troubleshooting steps, incorrect policy guidance, bad code suggestions, or incorrect compliance statements.
Defensive guardrails:
- Ground important answers in approved sources (RAG + citations).
- Human review for high-impact outputs.
- Quality monitoring (sample real conversations weekly, track regressions).
AI Buzz companion read: AI Monitoring & Observability
💸 LLM10: Unbounded Consumption (denial-of-wallet)
What it is: uncontrolled usage that causes cost spikes, performance degradation, or outages. This can come from abuse, bad prompts, runaway tool loops, or poor rate limiting.
Defensive guardrails:
- Rate limits (per user, per org, per IP where appropriate).
- Hard budgets (max tokens, max tool calls, max steps, max time).
- Cost monitoring: cost per conversation, cost per task, and alerts on spikes.
- Graceful fallback when limits hit (draft-only, partial answers, human escalation).
✅ Copy/paste: OWASP 2025 GenAI Security Checklist (practical starter)
Use this checklist to get a strong baseline quickly.
🗂️ A) Inventory and ownership
- We have an inventory of AI apps, models, connectors, and data sources (including pilots and shadow AI).
- Each AI app has a named owner and an approved scope.
🔐 B) Data rules and privacy
- We have clear “Green/Yellow/Red” data rules for AI usage.
- We have retention controls and safe logging (redaction; limited access).
🛡️ C) Tool permissions (agents)
- Tools are read-only by default.
- Write/irreversible actions require human approval.
- We enforce allowlists and scopes (only specific repos/folders/projects).
🧪 D) Testing (before and after changes)
- We run a small red-team style test set for prompt injection, leakage, and unsafe outputs.
- We re-test after model/prompt/RAG/tool changes.
📈 E) Monitoring + incident response
- We monitor quality, safety, privacy, drift, latency, and cost.
- We have an AI incident playbook and quick “kill switches” (draft-only, disable tools).
🧪 Mini-labs (beginner exercises you can do this week)
Mini-lab 1: “Tricky content” prompt injection test
- Collect 10 real-world documents your AI reads (tickets, PDFs, KB pages).
- Create 10 safe “injection-like” test cases (no real secrets) and see if the system follows untrusted instructions.
- Fix by tightening boundaries: separate trusted instructions, reduce tool permissions, add approvals.
Mini-lab 2: “Log without secrets” review
- Review one week of logs for prompts/attachments/outputs.
- Identify where sensitive data appears.
- Add redaction + retention limits + restricted access.
Mini-lab 3: “Cost runaway” guardrails
- Set a max token limit per request and a max step/tool-call limit per run.
- Add alerts for unusual cost per user or per workflow.
- Test the fallback experience when limits are hit.
🚩 Red flags that should slow your rollout
- No inventory (you can’t name your models, data sources, and connectors).
- Agents can write/execute without approval.
- RAG sources are editable by many people with no review gates.
- Outputs are passed downstream (HTML/code/automation) without validation.
- No monitoring baseline and no incident plan.
🔗 Keep exploring on AI Buzz
📚 Further reading (official OWASP resources)
🏁 Conclusion
The OWASP 2025 Top 10 is valuable because it forces a simple truth: securing GenAI isn’t only about “the model.” It’s about the whole application—data, prompts, tools, retrieval, logging, monitoring, and incident response.
Start small, fix the highest-risk failure modes first (injection, leaks, unsafe output handling, excessive agency), and build a repeatable test + monitoring routine. That’s how teams scale GenAI safely.
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