Table of Contents

    OWASP Expands Its Security Lens: The Top 10 Risks for Large Language Model Applications

    Friendly AI

    The Open Worldwide Application Security Project (OWASP) has long been a guiding force in identifying and mitigating the most critical risks in software security. For over two decades, the OWASP Top 10 has shaped secure coding practices and influenced organizations worldwide to prioritize the vulnerabilities that matter most.

    Now, with the rapid rise of AI and generative technologies, OWASP is extending this expertise to a new frontier: Large Language Model (LLM) applications. The newly released OWASP Top 10 for LLM Applications 2025 highlights the unique threats and failure modes that come with deploying powerful AI systems in real-world environments.

    Why a Top 10 for LLMs?

    Large Language Models are not just tools for generating text; they are increasingly embedded in products, workflows, and decision-making systems. From customer service chatbots to autonomous agents that trigger actions in enterprise systems, LLMs interact with sensitive data, users, and other software components.

    This integration creates a new attack surface—one not adequately covered by traditional security frameworks. Just as SQL injection defined web security threats in the 2000s, prompt injection is emerging as the defining attack vector in the LLM era.

    OWASP's list is not just theoretical; it reflects real-world exploits, red-team research, and the unique challenges posed by AI systems. For a deeper dive into these dynamics, see our analysis of context window poisoning.

    The OWASP Top 10 for Large Language Model Applications (2025)

    Learn and Defend

    • LLM01:2025 Prompt Injection Crafted prompts can manipulate an LLM into revealing secrets, executing unauthorized actions, or bypassing safeguards.

    • LLM02:2025 Sensitive Information Disclosure LLMs may unintentionally expose regulated data, personal information, or internal secrets.

    • LLM03:2025 Supply Chain Vulnerabilities arise from reliance on external datasets, APIs, plugins, or models.

    • LLM04:2025 Data and Model Poisoning Attackers may poison pre-training, fine-tuning, or embedding datasets to bias or compromise outputs.

    • LLM05:2025 Improper Output Handling Failing to validate or sanitize outputs can lead to dangerous downstream actions, such as code execution.

    • LLM06:2025 Excessive Agency Granting LLMs too much autonomy (e.g., to execute scripts, make purchases, or control systems) can result in high-impact risks.

    • LLM07:2025 System Prompt Leakage Exposure of hidden or system prompts may reveal sensitive instructions and allow attackers to manipulate model behavior.

    • LLM08:2025 Vector and Embedding Weaknesses Compromises in vector databases or embeddings may allow data exfiltration, poisoning, or inference attacks.

    • LLM09:2025 Misinformation LLMs can confidently generate false or misleading outputs, which undermines trust and decision-making.

    • LLM10:2025 Unbounded Consumption Attackers may trigger excessive computation, API calls, or token use, leading to denial of service or unexpected costs.

    Frequently Asked Questions (FAQ)

    Q1. What is the OWASP Top 10 for LLM Applications? It’s a security-focused framework developed by OWASP to identify the most critical risks associated with Large Language Model applications. Similar to the classic OWASP Top 10, it provides a prioritized list of vulnerabilities — but tailored to AI and generative systems.

    Q2. Why is “Prompt Injection” such a big deal? Prompt injection is to LLMs what SQL injection was to web apps. By crafting malicious inputs, attackers can override instructions, access sensitive data, or trick the model into performing unsafe actions.

    Q3. How does data or model poisoning work? Attackers may insert manipulated data during training, fine-tuning, or embedding creation. Over time, this biases the model, introduces backdoors, or causes targeted harmful responses.

    Q4. What’s new in the 2025 list compared to earlier versions?

    • New categories include System Prompt Leakage (LLM07), Vector and Embedding Weaknesses (LLM08), Misinformation (LLM09), and Unbounded Consumption (LLM10).
    • Risks like “Overreliance” and “Insecure Plugin Design” have been absorbed into broader categories.

    Q5. How can developers mitigate these risks?

    • Validate and sanitize LLM inputs and outputs.
    • Restrict plugin and API access following the principle of least privilege.
    • Monitor for misuse, misinformation, or excessive consumption.
    • Secure vector databases and embedding pipelines.
    • Combine OWASP’s guidance with practical security tooling like Feluda's cybersecurity Genes.

    What This Means for Developers and Security Teams

    Just as the original OWASP Top 10 shaped secure coding for web applications, this LLM-specific list is poised to guide best practices in AI deployment. Some early takeaways include:

    • Threat modeling must adapt. Traditional frameworks rarely consider adversarial prompts, embeddings, or misinformation.
    • Guardrails aren’t enough. Alignment layers and safety fine-tuning alone won’t stop injection attacks or output misuse.
    • Defense-in-depth is critical. Secure plugin design, output validation, vector DB hardening, and access control must be standard.
    • Continuous red-teaming is essential. Like pen-testing for web apps, adversarial testing for LLMs should become routine.

    For organizations adopting AI workflows, exploring practical tooling like Feluda’s security-focused Genes (for example, the Prompt Cybersecurity Pack) can complement OWASP’s guidance with hands-on mitigations.

    Developer’s Checklist: Securing LLM Applications

    ✅ Action Why It Matters
    Sanitize Inputs and Outputs Always validate what goes into the model and what comes out. Treat LLM output like untrusted user input.
    Implement Least Privilege for Plugins Restrict plugin and API access to only what’s necessary. Avoid giving LLMs direct system or network control without safeguards.
    Rate Limit and Monitor Usage Set request limits and monitor unusual activity to reduce the risk of Unbounded Consumption and denial-of-service attacks.
    Protect Sensitive Data Prevent LLMs from leaking private or regulated data. Apply redaction, masking, and strict policy filters.
    Secure Vector Databases Harden vector DBs and embedding pipelines against poisoning, inference attacks, or unauthorized queries.
    Red-Team Regularly Test models against adversarial prompts, misinformation scenarios, and data poisoning techniques.
    Validate Outputs Before Use Improper Output Handling (LLM05) can allow malicious instructions to flow downstream. Ensure human or system validation of responses.
    Manage Agency Carefully Limit autonomy. Don’t allow LLMs to trigger actions like purchases or system changes without explicit safeguards.
    Monitor for Misinformation LLMs can generate false but convincing outputs. Cross-check critical information and introduce human-in-the-loop validation where accuracy is crucial.
    Log and Audit Everything Maintain a complete audit trail of prompts, outputs, embeddings, and system actions for compliance and forensic investigations.

    Looking Ahead

    OWASP’s work signals that the security community is taking AI risks seriously and framing them in actionable terms. As enterprises race to embed LLMs into products, frameworks like this will help balance innovation with responsibility.

    Just as the classic Top 10 reshaped web security, the OWASP Top 10 for LLMs is positioned to become the go-to reference for building safe, resilient, and trustworthy AI systems.

    The message is clear: AI is powerful, but it is not immune. Security must evolve with the technology.

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