OWASP Top 10 for LLM Applications
The Essential Security Guide
Large Language Models (LLMs) are powering the future but with great power comes new risks. The OWASP Top 10 for LLM Applications (2025) distills years of research, breaches, and lessons learned into the 10 critical threats you must understand if you’re building with AI. Here’s the plain-English breakdown—and how to defend against each.
OWASP Top 10 for LLM Applications: Plain-English Guide with Actionable Defenses
What It Is
The OWASP Top 10 for LLM Applications is a community standard highlighting the most critical security risks when building apps powered by large language models (LLMs).
The update reflects real-world incidents with prompt attacks, retrieval-augmented generation (RAG), embedding misuse, agent safety, and data leakage. For each risk, OWASP provides practical mitigations you can apply today.
The 10 Risks and How to Defend Against Them
1. Prompt Injection (LLM01)
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Problem: Malicious inputs steer the model off policy, exfiltrate data, or trigger tools.
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Defend: Treat prompts like untrusted code. Isolate tools, enforce allowlists, moderate input/output, and run adversarial testing.
2. Sensitive Information Disclosure (LLM02)
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Problem: PII, secrets, or proprietary data leak through responses, logs, or retrieval.
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Defend: Never embed secrets in prompts. Minimize retrieval scope, redact sensitive fields, log carefully, and give users opt-outs.
3. Supply Chain (LLM03)
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Problem: Compromised models, datasets, embeddings, or plugins enter the stack.
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Defend: Verify provenance, pin hashes, scan artifacts, and treat third-party models as software dependencies requiring review.
4. Data & Model Poisoning (LLM04)
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Problem: Fine-tuning or embedding corpora are poisoned to bias outputs or insert backdoors.
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Defend: Curate and validate datasets, monitor drift, gate sources for fine-tunes, and red-team before promotion.
5. Improper Output Handling (LLM05)
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Problem: Treating model output as safe, then executing it (HTML, SQL, code) leads to XSS, injection, or tool abuse.
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Defend: Sanitize and validate all outputs, sandbox generated code, and restrict tool privileges.
6. Excessive Agency (LLM06)
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Problem: Agents get broad permissions, making irreversible changes (like financial transfers).
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Defend: Apply least privilege, scope tools narrowly, require human approval for sensitive actions, and keep audit trails.
7. System Prompt Leakage (LLM07)
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Problem: Attackers extract hidden instructions or secrets from the system prompt.
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Defend: Assume prompts can leak. Keep them minimal, avoid sensitive tokens, detect/deny leak attempts, and rotate exposed credentials.
8. Vector & Embedding Weaknesses (LLM08)
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Problem: RAG pipelines and vector DBs can be poisoned, inverted, or misused.
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Defend: Enforce per-collection ACLs, validate ingest sources, sign/index docs, isolate multi-tenant spaces, and monitor anomalies.
9. Misinformation (LLM09)
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Problem: Models confidently hallucinate or spread unverified claims.
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Defend: Require citations, add verification steps, apply retrieval with trust scores, and route high-risk queries to human review.
10. Unbounded Consumption (LLM10)
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Problem: Unlimited queries or context cause runaway costs, DoS, or model cloning.
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Defend: Enforce quotas, token limits, caching, anomaly detection, and budget guards.
Design Checklist You Can Apply Today
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Threat model the whole loop: Inputs → LLM → tools/RAG → outputs → users.
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Segment capabilities: Keep safety policies separate from capability prompts; scope tools tightly.
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Moderate twice: Check both input and output. Always sandbox executable output.
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Harden RAG: Validate sources, sign documents, enforce ACLs, monitor retrieval drift.
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Continuously test: Automate red-teaming, track jailbreak/poison success rates, regression-test defenses.
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Govern changes: Treat prompt edits, model swaps, and dataset updates like code changes—review, approve, and roll back when needed.
What Changed from 2023/24 to 2025
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New inclusions: System Prompt Leakage and Vector & Embedding Weaknesses—reflecting rising RAG/agent incidents.
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Elevated: Sensitive Information Disclosure now a top-priority risk.
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Consolidated: Model DoS folded into Unbounded Consumption.
How to Adopt the OWASP LLM Top 10 in Your Org
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Map features to risks: Identify which of the 10 apply to each workflow.
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Define controls: Assign policies, owners, and technical defenses.
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Instrument & alert: Track moderation hits, tool calls, retrieval anomalies, and cost spikes.
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Gate launches: Run adversarial tests tied to each risk before release.
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Review quarterly: Models, prompts, and data evolve—so must your defenses.
Sources & Further Reading
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OWASP Top 10 for LLM Applications (official PDF)
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Research on Prompt Injection, Vector Weaknesses, and Misinformation from OWASP and security labs
Final Word
The OWASP Top 10 for LLM Applications is a practical roadmap for anyone deploying AI safely. By mapping your architecture to these ten risks and adopting layered defenses, you’ll move from reactive firefighting to proactive resilience—keeping your users, data, and brand protected in the fast-evolving world of AI.
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FAQ's
What exactly is “OWASP Top 10 for LLM Applications”?
Yes, it’s very similar in spirit to the OWASP Web Application Top 10 - a prioritized list of the biggest security risks when you build apps using large language models. It’s maintained by the OWASP GenAI Security Project (with wide community input). The 2025 version expands risks in things like prompt attacks, data leakage, embedding/vector misuse, agent safety, etc.
Why is prompt injection ranked #1? Have people seen real attacks?
Prompt injection is foundational because it allows malicious inputs to override or subvert model rules or safety layers. Yes, both research and practice have observed these attacks. For instance, in discussions around “OWASP Top 10 Risks for Large Language Models 2025,” prompt injection is repeatedly highlighted.
What is “system prompt leakage” and how big a risk is it?
System prompt leakage means hidden instructions (system rules, safety policies, etc.) or embedded secrets might be extracted by users via clever prompts. The damage could include bypassing all your intended safety layers, exposing internal logic, or even giving someone a blueprint for further attacks. The 2025 OWASP update added it as a distinct risk due to real incidents or threat modeling showing its importance.
How do vector / embedding weaknesses matter in practice?
Embeddings are used for similarity search, retrieval (RAG), etc. Weaknesses include poisoning the embedding space (so malicious or biased content is retrieved), namespace mix-ups (wrong document pulled), or insecure storage/access. This makes RAG pipelines a big attack surface. The OWASP Top 10 calls them out (LLM08) and forum posts highlight that many organizations lack strong checks in ingestion and retrieval.
What are actionable, realistic defenses — especially for small teams or open-source developers?
Some of the more practical defenses include:
- Enforcing input/output moderation and filtering.
- Minimizing secrets in prompts; rotating keys.
- Using least privilege for model “tools” or agents.
- Validating data sources for RAG / embeddings.
- Logging and monitoring anomalous behavior.
- Running red-team tests or benchmarks (even simple ones) to see where the system fails. Forum users often recommend starting with what you control (prompts, tool access) and gradually improving infrastructure defenses.
Can we completely eliminate these risks, or are they always present to some degree?
No, you generally can’t eliminate them all. The consensus is that LLM apps always have residual risk. What organizations can do is manage the risk: reduce severity, detect when things go wrong, limit exposure, and respond quickly. The OWASP framework expects continuous improvement and layered security rather than a “one-and-done” fix.
How do you test or measure your app against the OWASP Top 10?
Yes. People mention:
- Using red-team scenarios tailored to each OWASP risk.
- Benchmarks or test suites (public/open source) that simulate prompt injection, data leakage, etc.
- Monitoring tools for retrieval anomalies in RAG pipelines.
- Regular reviews of prompt safety, output handling, and access control.
- Logging and auditing to see how model outputs behave under stress or unusual inputs.
What changed in the 2025 version compared to earlier versions?
The 2025 update adds or elevates several risks: Sensitive Information Disclosure is more emphasized; System Prompt Leakage is newly treated as its own category; Vector & Embedding Weaknesses gets stronger attention because of the rise of RAG/embedding usage; Unbounded Consumption is more clearly defined (cost, DoS, token abuse). Some older risks were consolidated.
Which risks tend to be overlooked or underestimated by developers?
Risks that often get less attention:
- Data poisoning (training/fine-tune/embedding).
- Supply chain issues (third-party models or plugins).
- Improper output handling (executing code or rendering responses without sanitization).
- Excessive agency (giving the model tools that have too much autonomy).
- Unbounded consumption (cost, performance, token abuse).
Forum threads often show developers focusing heavily on prompt behavior but overlooking downstream systems like vector stores, logging, permissions, etc.
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