If you are building AI agents in production, the OWASP Top 10 for LLMs and GenAI is not optional reading. It is one of the clearest, community-driven security baselines for how modern LLM and agentic systems fail, and how teams should reduce those failures in real deployments.12
OWASP now positions this work under the broader GenAI Security Project, explicitly covering LLMs, agentic AI systems, and AI-driven applications. That scope makes it directly relevant for agent architects, platform teams, security leaders, and engineering executives making delivery and governance decisions.13
Executive Summary
The 2025 OWASP Top 10 identifies ten recurring risk classes that show up across the LLM and GenAI application lifecycle: development, deployment, and operations.2
The main executive takeaway is simple: most critical failures in agent systems are not model-IQ problems. They are systems-security and control-plane problems.
When translated to agent development, the OWASP list says that teams must secure:
- inputs and instructions (prompt injection, system prompt leakage)
- data and model lineage (supply chain, data/model poisoning)
- tool execution boundaries (improper output handling, excessive agency)
- retrieval and memory surfaces (vector and embedding weaknesses)
- decision quality and operational resilience (misinformation, unbounded consumption)
- confidentiality controls (sensitive information disclosure)
In short, safe agent development is less about one strong prompt and more about layered controls around planning, tool use, memory, identity, validation, and monitoring.
OWASP 2025 Top 10 Through an Agent Development Lens
| OWASP risk | What it means in practice | Why agent teams should care |
|---|---|---|
| LLM01: Prompt Injection | Malicious or crafted input manipulates model behavior | Agents can be pushed into unsafe tool calls, policy bypass, or hidden instruction following |
| LLM02: Sensitive Information Disclosure | Confidential data leaks through prompts, context, or outputs | Agents often touch internal knowledge, APIs, and user data, increasing leak impact |
| LLM03: Supply Chain | Compromised models, datasets, plugins, or dependencies | Agent stacks depend on many third-party components and connectors |
| LLM04: Data and Model Poisoning | Training/fine-tuning/embedding data is tampered with | Poisoned knowledge can silently alter agent behavior and trustworthiness |
| LLM05: Improper Output Handling | LLM output is consumed without validation/sanitization | Unsafe outputs can become executable actions in downstream systems |
| LLM06: Excessive Agency | Agents get too much autonomy or privilege | Over-scoped permissions turn normal mistakes into high-impact incidents |
| LLM07: System Prompt Leakage | Hidden instructions are exposed and exploited | Revealed policy logic makes bypass attempts easier and more targeted |
| LLM08: Vector and Embedding Weaknesses | Weaknesses in retrieval/indexing pipelines | RAG-enabled agents can be manipulated through poisoned or adversarial retrieval content |
| LLM09: Misinformation | Confident but incorrect outputs influence decisions | Agentic automation can propagate bad decisions at machine speed |
| LLM10: Unbounded Consumption | Uncontrolled usage of tokens/compute/tooling | Agent loops and broad tool access can create cost spikes and service instability |
Risk names and descriptions above are based on the OWASP 2025 Top 10 content and linked risk pages.24
Why This Is Essential Reading for Agent Development
- It gives a shared language across engineering, security, and leadership for AI risk prioritization.
- It reframes agent failures as architecture and governance issues, not only model behavior issues.
- It aligns security planning to the full lifecycle (build, deploy, operate), which is where most agent incidents emerge.
- It is directly connected to OWASP's broader agentic security work, making it practical for teams moving from LLM apps to autonomous agents.15
What to Do Next (Executive Action)
- Adopt the OWASP Top 10 as a baseline control framework for all new agent initiatives.
- Map each agent workflow to the Top 10 and identify where controls are currently missing.
- Prioritize guardrails around tool invocation, privilege boundaries, output validation, and retrieval integrity first.
- Add continuous monitoring for prompt attacks, data leakage, cost anomalies, and unsafe autonomous actions.
- Re-review architecture decisions whenever agent scope or autonomy increases.
Agent development is now a security-critical discipline. Teams that treat this OWASP guidance as foundational design input, rather than post-launch compliance, will ship faster with fewer high-severity failures.
Footnotes
-
OWASP Foundation. "OWASP Top 10 for Large Language Model Applications." ↩ ↩2 ↩3
-
OWASP GenAI Security Project. "2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps." ↩ ↩2 ↩3
-
OWASP GenAI Security Project. "OWASP Top 10 for LLM is now the GenAI Security Project and promoted to OWASP Flagship status" (March 26, 2025). ↩
-
OWASP GenAI Security Project. "LLM01:2025 Prompt Injection." ↩
-
OWASP GenAI Security Project. "OWASP Agentic Security Initiative (ASI)." ↩
