The OWASP Top 10 for Large Language Model Applications is the industry-standard list of security risks for AI systems. This guide explains each risk in plain English, shows how real attacks work, and describes the defenses that hold up in practice, based on what our AI red team sees in production LLM applications.
01 //What the OWASP LLM Top 10 Is
OWASP (the Open Worldwide Application Security Project) maintains the industry-standard 'Top 10' lists of critical security risks. The OWASP Top 10 for LLM Applications applies this to generative AI: chatbots, RAG systems, and AI agents. It exists because traditional AppSec testing misses AI-specific failure modes: LLMs are probabilistic, they act on natural-language instructions, and they can be manipulated through the very data they are designed to read.
02 //The Ten Risks, With Real Attacks
Each risk in plain English, with how the attack actually works. At Faltrox, every AI/LLM penetration test maps directly to this list so findings are standardized and comparable across engagements.
- LLM01 Prompt Injection: input that overrides the system prompt, e.g. hidden 'ignore your rules' text in a document the AI reads. Defense: input/output filtering, privilege separation, treat model output as untrusted.
- LLM02 Sensitive Information Disclosure: the model reveals PII, secrets, or its own system prompt. Defense: output scrubbing, data minimization, strict RAG access controls.
- LLM03 Supply Chain: a compromised third-party model, dataset, or plugin adds a backdoor. Defense: vet and pin provenance, scan dependencies.
- LLM04 Data & Model Poisoning: malicious training/fine-tuning data corrupts behavior. Defense: validate and source data, monitor for anomalies.
- LLM05 Improper Output Handling: app passes model output to a shell, SQL query, or browser, causing RCE or XSS. Defense: validate and encode all output downstream.
- LLM06 Excessive Agency: an agent has more permission or autonomy than needed and is tricked into misusing it. Defense: least-privilege tools, scoped functions, human-in-the-loop.
- LLM07 System Prompt Leakage: the confidential system prompt is extracted. Defense: never store secrets in prompts; assume the prompt is public.
- LLM08 Vector & Embedding Weaknesses: RAG embedding flaws leak or poison data across tenants. Defense: isolate and access-control vector stores per tenant.
- LLM09 Misinformation: confident but false output ('hallucination') users act on. Defense: grounding, citations, human review for high-stakes output.
- LLM10 Unbounded Consumption: attackers drive runaway compute or 'denial of wallet'. Defense: rate limiting, quotas, cost monitoring.
03 //How to Test Against the Top 10
Knowing the risks is not the same as being secure. LLM behavior is non-deterministic, so a control that works once may fail on the next input. AI systems need dedicated adversarial testing (AI red teaming), not just a checklist review. Faltrox's AI & LLM security testing runs statistical fuzzing and manual jailbreak crafting against each OWASP LLM risk, verifies findings are reproducible, and delivers hardened system prompts and guardrail configurations.
