OWASP LLM Top 10 Explained for Engineering Teams

Introduction to the OWASP LLM Top 10

The OWASP LLM Top 10 is a community-driven project that identifies the most critical security risks facing applications that use large language models. Released and updated by the Open Worldwide Application Security Project, the list helps engineering teams, security professionals, and business leaders understand where LLM systems are most likely to fail. Unlike generic AI safety discussions, the OWASP list focuses on practical, exploitable vulnerabilities with clear mitigation strategies.

Why does this matter now? LLMs are being integrated into customer support, code generation, search, documentation, and autonomous agents. Each integration creates new trust boundaries. A model that can read email, query databases, or execute code becomes a high-value target. The OWASP framework gives teams a common language to discuss these risks and prioritize defenses.

This guide explains each of the ten risks in plain language, provides real-world examples, and offers actionable controls. Whether you are building your first LLM feature or scaling an AI platform, understanding these risks is essential for shipping secure products. By aligning your security program with the OWASP LLM Top 10, you can demonstrate due diligence to customers, auditors, and regulators while reducing the chance of a costly security incident.

LLM01: Prompt Injection

Prompt injection occurs when an attacker manipulates the model through crafted input. The input may override system instructions, bypass safety filters, or trick the model into revealing sensitive information. Because LLMs process instructions and data in the same context window, separating trusted developer instructions from untrusted user content is fundamentally difficult.

Direct prompt injection targets the user input channel. Indirect prompt injection hides malicious instructions in external content such as emails, documents, or web pages that the model later processes. Both forms can lead to data leakage, unauthorized actions, and harmful output. The OWASP project ranks this risk first because it is both common and difficult to eliminate completely.

Mitigation requires layered controls. Validate and sanitize inputs, treat system prompts as sensitive configuration, separate instructions from data, and apply output filtering. For applications with tool access, enforce strict schemas, allowlists, and human confirmation for high-impact actions. Regular prompt injection testing helps identify bypasses before attackers do. No single control eliminates prompt injection, but combining multiple defenses raises the cost and complexity of successful attacks.

LLM02: Insecure Output Handling

LLM output is often passed directly to downstream systems without validation. Insecure output handling occurs when that output is used in unsafe ways, such as rendering unsanitized content in a browser, executing generated code, or inserting text into database queries. This risk is essentially the traditional injection problem applied to model-generated content.

For example, an LLM summarizing a support ticket might produce output containing HTML or JavaScript. If that output is rendered in an admin dashboard, it could lead to cross-site scripting. Similarly, a coding assistant that generates shell commands without sandboxing could compromise the developer's machine. The risk grows when LLM output is consumed by automated systems rather than human reviewers.

Controls include output encoding, content security policies, parameterized queries, and sandboxed execution. Treat all model output as untrusted until validated. If the application displays LLM output to users, apply the same sanitization you would use for any user-generated content. Where possible, avoid passing LLM output directly to interpreters, command shells, or database query constructors.

LLM03: Training Data Poisoning

Training data poisoning affects the model before it ever reaches production. An attacker modifies or injects data into the training set to influence the model's behavior. The effects can be subtle, such as biased responses, or overt, such as backdoors that trigger on specific inputs.

This risk is especially relevant for models that are fine-tuned on proprietary or third-party datasets. If the data pipeline is not secured, poisoned examples can enter through crowdsourced data, scraped web content, or compromised data vendors. Detecting poisoning after training is difficult because the harmful behavior may only appear under specific conditions.

Mitigation starts with data provenance. Track where training data comes from, validate data sources, and use anomaly detection to identify suspicious patterns. Red teaming and behavioral testing after training can reveal some poisoning, but prevention at the data ingestion stage is more effective. Restrict who can contribute to training datasets and require review for data from external or untrusted sources.

LLM04: Model Denial of Service

Model denial of service exploits the computational cost of running LLMs. Attackers send inputs designed to consume excessive memory, CPU, or API quota. Long inputs, recursive prompts, and resource-intensive tasks can degrade service for legitimate users or inflate operating costs.

This risk is amplified for public-facing APIs and multi-tenant platforms. A single abusive user can exhaust rate limits, delay responses, or trigger vendor charges. In some cases, the attack may be used to force a model provider to throttle or block an application.

Controls include input length limits, rate limiting, timeout policies, and cost monitoring. Use quotas per user or API key and implement circuit breakers for resource-intensive operations. Consider model output token limits and chargeback mechanisms to align usage with cost. Alerting on unusual usage patterns helps detect attacks early and prevent runaway expenses. Multi-tenant platforms should isolate resource consumption so one tenant cannot starve others.

LLM05: Supply Chain Vulnerabilities

LLM supply chains include pretrained models, fine-tuning datasets, third-party libraries, vector databases, and model hosting services. A vulnerability in any of these components can compromise the entire application. Supply chain risks are not unique to LLMs, but the opacity of model provenance makes them harder to assess.

Examples include using a pretrained model from an untrusted source, loading a malicious adapter or LoRA, depending on a vulnerable embedding library, or using a compromised vector database plugin. Each component introduces trust assumptions that may not be obvious to the engineering team.

Mitigation includes software bill of materials for models and datasets, dependency scanning, vendor security reviews, and pinned versions. Prefer reputable model providers, verify checksums, and isolate third-party components where possible. Treat model artifacts with the same rigor as other production dependencies. Regularly audit your supply chain for new vulnerabilities and outdated components.

LLM06: Sensitive Information Disclosure

Sensitive information disclosure covers both training data leakage and accidental exposure through the context window. LLMs can memorize training data and regurgitate it when prompted. They can also leak information from retrieved documents, conversation history, or system prompts if access controls are weak.

For example, a healthcare assistant fine-tuned on patient records might reveal personal information under repetition attacks. A retrieval system connected to shared documents might return content the user is not authorized to see. System prompt extraction can reveal implementation details that help attackers craft better attacks. Data classification and need-to-know access should apply to LLM context just as they do to source systems.

Controls include data minimization in training sets, differential privacy techniques, access-controlled retrieval, and output filtering. Regular testing for data leakage helps identify memorization issues. Avoid placing secrets, internal instructions, or sensitive context where users can influence or extract them. When retrieval is used, enforce the same access controls that protect the original documents.

LLM07: Insecure Plugin Design

Plugins and tools extend what an LLM can do. Insecure plugin design occurs when these extensions do not validate inputs, enforce permissions, or handle failures safely. A plugin that accepts free-form parameters from the model is particularly risky because prompt injection can translate directly into plugin abuse.

For example, a plugin that sends emails might accept arbitrary recipient and body parameters. A plugin that queries a database might construct SQL from model output. Without strict schemas and validation, these plugins become remote-control interfaces for attackers.

Controls include parameterized plugin inputs, least-privilege access, explicit user confirmation for high-impact actions, and clear error handling. Plugins should be designed as if the model input is untrusted, because in many cases it is. Avoid plugins that perform destructive or irreversible actions without a second authorization factor.

LLM08: Excessive Agency

Excessive agency refers to giving an LLM more autonomy than necessary. When models can take actions without human oversight, the impact of prompt injection or model errors increases. An autonomous agent that can browse, purchase, send messages, or modify records requires strong guardrails.

Real-world concerns include agents that book unauthorized travel, make fraudulent purchases, or send confidential information to external addresses. Even benign actions can cause harm if taken at scale or in the wrong context.

Mitigation involves principle of least privilege. Limit the tools and actions available to the model. Require approval for irreversible or high-risk actions. Maintain audit logs of all decisions and provide users with visibility into what the agent is doing. Design agent workflows so that critical decisions are escalated to humans rather than delegated entirely to the model.

LLM09: Overreliance

Overreliance is the human and organizational risk of trusting LLM output too much. Models can produce confident but incorrect information, known as hallucinations. In security-critical contexts, acting on bad advice can introduce vulnerabilities rather than reduce them.

For example, a developer might ask an LLM to generate authentication code and deploy it without review. The code might look correct but contain subtle flaws. A security analyst might trust a model-generated threat assessment and miss real risks. Organizations should establish policies that define when LLM output must be reviewed by qualified humans.

Controls include human review of high-stakes output, clear disclaimers, citations where possible, and feedback loops. Applications should communicate uncertainty and direct users to authoritative sources for critical decisions. In security contexts, never deploy LLM-generated code, configurations, or assessments without expert validation.

LLM10: Model Theft

Model theft involves extracting model weights, architecture, or behavior. Attackers may use repeated queries to clone a model, steal proprietary fine-tuning, or reconstruct training data. This risk is most relevant for models that represent significant intellectual property or competitive advantage.

Extraction attacks can be surprisingly efficient. An attacker queries the model with diverse inputs and trains a smaller model to mimic its behavior. While the copy may not be perfect, it can capture enough functionality to undermine business value.

Controls include rate limiting, output perturbation, query logging, watermarking, and legal protections. For high-value models, consider hosting restrictions, authentication, and monitoring for suspicious query patterns. Terms of service and technical enforcement together create stronger protection than either approach alone. Model theft is not only a technical risk but also a business risk that should be covered in contracts and intellectual property strategy.

Mapping OWASP LLM Risks to Your Application

Not every LLM application faces the same risk profile. A read-only content generator has different concerns than an autonomous agent with database access. The first step in applying the OWASP LLM Top 10 is mapping each risk to your specific architecture and use case.

Start with a data flow diagram. Identify where user input enters the system, where external data is retrieved, where the model is called, and where model output is used. Mark each trust boundary. Then walk through the top ten risks and ask whether your design has controls for each.

For prompt injection, consider both direct user input and indirect content from email, documents, or web pages. For insecure output handling, look at where model output is rendered, executed, or stored. For sensitive information disclosure, review what data is in the training set, the retrieval corpus, and the context window.

This mapping exercise produces a risk register. Each risk gets a likelihood and impact rating, existing controls, gaps, and planned mitigations. The register becomes the foundation for your LLM security roadmap and helps justify investment to leadership. Update the register quarterly or whenever the application architecture changes significantly.

Testing for OWASP LLM Top 10 Risks

Testing should cover both automated and manual techniques. Automated tools can scan for known prompt injection patterns, detect unsafe output handling, and fuzz inputs for denial of service conditions. They are efficient for regression testing and continuous integration.

Manual testing is needed for business logic flaws and novel attack chains. A skilled tester will attempt to bypass filters using encoding, translation, and multi-turn conversation. They will test plugin boundaries, probe for data leakage, and evaluate the real impact of successful exploitation.

Penetration testing reports should align findings with the OWASP LLM Top 10. This makes results actionable for engineering teams and demonstrates due diligence to customers and auditors. Each finding should include reproduction steps, evidence, severity, and remediation guidance.

Retesting after remediation is essential. A fix for one risk can introduce bypasses elsewhere. For example, stronger output filtering might be evaded by changing the prompt format. Continuous testing ensures that your defenses remain effective as the model and application evolve. Engage an external red team at least annually to challenge internal assumptions and discover blind spots.

Building an LLM Security Program

Addressing the OWASP LLM Top 10 requires more than ticking items off a list. It requires a program that combines design review, testing, monitoring, and incident response. Start by inventorying your LLM applications, data flows, and trust boundaries. Identify which risks apply based on your architecture and threat model. Not every application faces all ten risks equally, so focus on the ones with the highest business impact.

Integrate security into the development lifecycle. Review designs before implementation, test prototypes for prompt injection and data leakage, and perform red team exercises before major releases. Use automated scanning for continuous regression detection and manual testing for novel attack paths. Security should be part of the AI product roadmap, not an afterthought.

Establish clear ownership. The AI engineering team, security team, and legal or compliance team all have roles. Engineering owns secure design and implementation. Security owns testing and risk assessment. Legal owns terms of use, data handling, and regulatory alignment. Without clear ownership, gaps emerge between teams.

Finally, measure and improve. Track the rate of successful injections, the number of sensitive outputs blocked, time to remediate findings, and model behavior over time. A mature program treats LLM security as a continuous discipline, not a one-time audit. Regular reporting to leadership ensures that security investments keep pace with product growth.

For software companies shipping LLM products, the OWASP LLM Top 10 is the best starting point for building customer trust and meeting enterprise security expectations. EncryptSec helps companies assess, test, and remediate these risks through specialized AI red teaming and LLM security testing services.

By proactively testing against all 10 categories, organizations can build AI applications that are resilient, compliant, and trustworthy. As LLMs continue to integrate deeper into business workflows, security must be treated as a first-class engineering concern rather than an afterthought. EncryptSec's AI security services help companies identify and remediate these risks before attackers exploit them.