As Large Language Models (LLMs) become increasingly integral to applications ranging from chatbots and customer support to financial services and healthcare, their vulnerabilities have become a prime target for malicious actors. Security issues in these systems can lead to devastating consequences including data breaches, unauthorized access, and large-scale system manipulation.

The OWASP Top 10 for LLM Applications provides a critical framework for understanding the most pressing threats to AI-powered systems. In this article, we explore each of the ten vulnerabilities in detail, examining how they work, what real-world risks they present, and how organizations can mitigate them effectively.

Why LLM Security Matters

LLMs are no longer experimental tools confined to research labs. They are deployed in production environments where they handle sensitive data, make decisions, and interact with downstream systems. This shift from novelty to infrastructure means that the attack surface has grown dramatically.

Unlike traditional software vulnerabilities, LLM weaknesses often stem from the fundamental nature of how these models learn, reason, and generate output. They can be exploited not just through code, but through language itself. That makes them uniquely challenging to defend.

Understanding these vulnerabilities is the first step toward building resilient AI systems. The following ten categories represent the most critical risks organizations should address.

1. Prompt Injection

Prompt injection is widely regarded as the most critical vulnerability in LLM applications. It occurs when an attacker crafts specific inputs that manipulate the model into behaving in unintended ways, bypassing built-in safety measures, or disclosing sensitive information.

There are two primary forms of this attack:

  • Direct prompt injection (jailbreaking): The attacker provides input that overwrites or bypasses the system prompt, causing the model to ignore its instructions and follow the attacker's commands instead
  • Indirect prompt injection: Malicious instructions are embedded in external data sources that the model processes, such as web pages, documents, or API responses, causing the model to act on the attacker's behalf without the user's knowledge

For example, an attacker could embed hidden instructions in a web page that an LLM-powered search tool retrieves. When the model processes that page, it unknowingly follows the injected instructions, potentially leaking user data or performing unauthorized actions.

Mitigation Strategies

  • Implement strict input validation and sanitization for all prompts
  • Use privilege separation to limit what the model can access or execute
  • Enforce human-in-the-loop controls for sensitive operations
  • Monitor and log all model interactions for anomalous behavior

2. Insecure Output Handling

Insecure output handling occurs when an LLM's generated output is passed to other system components without proper validation or sanitization. Because LLMs can produce arbitrary text, treating their output as trusted input to downstream systems is inherently dangerous.

This vulnerability effectively provides attackers with indirect access to backend functionality. If the model's output is rendered in a browser, it can lead to cross-site scripting (XSS). If it is executed in a system shell, it can enable remote code execution. If it is passed to a database query, it can facilitate SQL injection.

The root cause is treating LLM output as inherently safe when it should be treated with the same suspicion as any user-supplied input.

Mitigation Strategies

  • Apply output encoding and escaping appropriate to the downstream context
  • Never pass LLM output directly to system commands, databases, or rendering engines without sanitization
  • Implement content security policies that restrict execution of generated content
  • Use allowlists to constrain the format and content of model outputs

3. Training Data Poisoning

Training data poisoning occurs when the datasets used to train or fine-tune an LLM are deliberately tampered with. This can introduce backdoors, biases, false information, or other malicious behaviors into the model that persist through deployment.

Because LLMs learn patterns from vast amounts of data, even small amounts of poisoned content can have outsized effects. An attacker who can influence training data can shape how the model responds to specific queries, introduce subtle biases that degrade decision quality, or create hidden triggers that activate malicious behavior under specific conditions.

This is particularly dangerous because the effects of data poisoning are often invisible during standard testing. The model may perform normally on most inputs while producing compromised outputs only in specific, attacker-controlled scenarios.

Mitigation Strategies

  • Verify the provenance and integrity of all training data
  • Implement data validation pipelines that detect anomalies and adversarial examples
  • Use diverse data sources to reduce the impact of any single compromised source
  • Conduct regular audits of model behavior to detect drift or unexpected outputs

4. Model Denial of Service

Model Denial of Service (DoS) attacks exploit the resource-intensive nature of LLM inference to degrade or disable AI services. Unlike traditional DoS attacks that overwhelm network bandwidth, these attacks target the computational cost of processing complex queries.

Attackers can trigger disproportionate resource consumption through several techniques:

  • Flooding: Submitting large volumes of requests to exhaust computational resources
  • Expensive queries: Crafting inputs that require maximum processing time, such as extremely long prompts or queries that force recursive reasoning
  • Resource exhaustion: Triggering costly computational chains where the model repeatedly calls external services or processes large datasets

For organizations running LLMs at scale, these attacks can inflict significant financial damage through inflated cloud computing costs, even if the service remains technically available.

Mitigation Strategies

  • Implement rate limiting and request throttling per user and per session
  • Set hard limits on input length and computational budget per request
  • Monitor resource consumption patterns and alert on anomalies
  • Use queuing systems to manage burst traffic and prioritize legitimate requests

5. Supply Chain Vulnerabilities

Supply chain vulnerabilities in LLM applications arise from the extensive reliance on third-party components, including pre-trained models, datasets, plugins, and libraries. If any component in this chain is compromised, the integrity of the entire system can be undermined.

The LLM supply chain is uniquely vulnerable because:

  • Pre-trained models: Organizations frequently use models trained by third parties. A compromised model could contain hidden backdoors that allow attackers to manipulate outputs under specific conditions
  • Training datasets: Publicly available datasets used for fine-tuning may contain poisoned data that introduces vulnerabilities
  • Plugins and extensions: Third-party integrations can introduce security gaps if they are not thoroughly vetted
  • Libraries and frameworks: Vulnerabilities in underlying ML frameworks can affect all models built on them

The PoisonGPT attack, demonstrated by Mithril Security, showed how a seemingly legitimate pre-trained model could be subtly modified to spread disinformation while performing normally on standard benchmarks. This illustrates how difficult supply chain attacks can be to detect.

Mitigation Strategies

  • Verify the source and integrity of all third-party models and datasets
  • Use cryptographic signing and checksums to detect tampering
  • Maintain an inventory of all dependencies and monitor them for known vulnerabilities
  • Conduct independent validation of model behavior before deployment

6. Sensitive Information Disclosure

Sensitive information disclosure occurs when an LLM inadvertently reveals confidential data through its outputs. This can happen when the model was trained on datasets containing private information, when it memorizes and later reproduces sensitive content from its training data, or when it is prompted in ways that elicit protected information.

The risk is amplified by the fact that LLMs can memorize and reproduce verbatim snippets from their training data. If that data included personal information, proprietary code, internal documents, or credentials, the model may leak them in response to carefully crafted queries.

This vulnerability creates serious compliance risks under regulations like GDPR, HIPAA, and CCPA, where the unauthorized exposure of personal data can result in significant legal and financial consequences.

Mitigation Strategies

  • Scrub training data to remove sensitive, personal, and proprietary information
  • Implement output filtering to detect and block disclosure of confidential content
  • Use differential privacy techniques during training to limit memorization
  • Restrict model access to sensitive data stores through strict access controls

7. Insecure Plugin Design

LLM plugins and extensions allow models to interact with external systems, access real-time data, and perform actions beyond text generation. However, poorly designed plugins can create significant exploitation opportunities.

When plugins process untrusted inputs without adequate validation, attackers can leverage them to bypass security boundaries. The LLM effectively becomes a proxy for exploiting the plugin's capabilities, allowing attackers to perform actions such as data exfiltration, privilege escalation, or remote code execution.

The challenge is compounded by the fact that plugins often have elevated permissions that the LLM itself should not have access to. A single vulnerable plugin can expose the entire system to compromise.

Mitigation Strategies

  • Apply the principle of least privilege to all plugin permissions
  • Validate and sanitize all inputs passed from the LLM to plugins
  • Require explicit user authorization for sensitive plugin actions
  • Conduct security reviews and penetration testing of all plugins before deployment

8. Excessive Agency

Excessive agency refers to the risk of granting LLMs too much capability, too many permissions, or too much autonomy in their interactions with external systems. This vulnerability manifests in three distinct categories:

  • Excessive functionality: The model has access to capabilities beyond what is necessary for its intended purpose, such as the ability to modify files, execute system commands, or interact with databases
  • Excessive permissions: The model operates with elevated privileges that allow it to perform actions the user did not intend or authorize, such as reading sensitive files or modifying system configurations
  • Excessive autonomy: The model can take high-impact actions without requiring human verification, potentially leading to destructive or irreversible changes

When an LLM has excessive agency, any other vulnerability, from prompt injection to insecure plugin design, becomes significantly more dangerous because the blast radius of a successful exploit is much larger.

Mitigation Strategies

  • Limit model capabilities to only what is strictly required for each use case
  • Apply the principle of least privilege to all system integrations
  • Require human approval for any high-impact or irreversible actions
  • Implement logging and monitoring for all actions the model takes on external systems

9. Overreliance

Overreliance occurs when users or organizations trust LLM outputs without appropriate verification, treating the model as an authoritative and infallible source. Because LLMs generate confident-sounding text regardless of accuracy, they can produce inaccurate information, hallucinate facts, or generate inappropriate content, all while appearing entirely credible.

This vulnerability is not purely technical. It is a human and organizational risk. When teams build workflows that depend on LLM outputs without review processes, they create systemic exposure to:

  • Incorrect decisions: Acting on hallucinated data or flawed analysis
  • Security gaps: Trusting AI-generated security assessments or code reviews that miss critical issues
  • Legal liability: Publishing or acting on AI-generated content that is factually wrong or defamatory
  • Reputational damage: Disseminating misinformation through automated channels

Mitigation Strategies

  • Establish clear guidelines for when and how LLM outputs should be reviewed by humans
  • Implement cross-referencing workflows that validate critical AI-generated content
  • Educate users about LLM limitations, including the tendency to hallucinate
  • Use confidence scores and uncertainty indicators where available

10. Model Theft

Model theft refers to the unauthorized access, copying, or extraction of proprietary LLMs. Attackers may steal the model weights, architecture, or the intellectual property embedded within the model. This can result in the loss of competitive advantage, exposure of sensitive information encoded in the model, or the illegal redistribution of the model itself.

Model theft can occur through several vectors:

  • Direct access: Exploiting insufficient access controls to download model files
  • Model extraction attacks: Querying the model systematically to reconstruct a functional replica based on its outputs
  • Side-channel attacks: Exploiting hardware or infrastructure vulnerabilities to access model parameters
  • Insider threats: Employees or contractors with access to model artifacts exfiltrating them

For organizations that have invested significant resources in developing proprietary models, model theft represents a direct economic threat. The stolen model can be used by competitors, sold on underground markets, or analyzed to discover additional vulnerabilities.

Mitigation Strategies

  • Implement strong access controls and authentication for all model endpoints
  • Use rate limiting and query monitoring to detect extraction attempts
  • Apply watermarking techniques to model outputs for traceability
  • Encrypt model artifacts at rest and in transit

Building a Comprehensive Defense Strategy

Addressing these vulnerabilities requires more than fixing individual issues in isolation. Organizations need a holistic approach to LLM security that spans the entire lifecycle, from data collection and training through deployment and ongoing monitoring.

Key principles for building a robust defense include:

  • Defense in depth: Layer multiple security controls so that no single point of failure can compromise the system
  • Zero trust for AI outputs: Treat all LLM-generated content as untrusted input that must be validated before use
  • Continuous monitoring: Implement real-time monitoring of model behavior, resource consumption, and output patterns
  • Incident response planning: Develop specific response procedures for AI-related security incidents
  • Regular assessment: Conduct periodic security audits and red team exercises focused on AI-specific attack vectors

Closing Thoughts

The rapid adoption of LLMs across industries has created an entirely new category of security challenges. The OWASP Top 10 for LLM Applications provides a valuable starting point for understanding these risks, but the threat landscape is evolving rapidly.

Organizations deploying LLMs must recognize that these systems require the same rigor in security assessment that traditional software does, with the added complexity of defending against attacks that operate through language rather than code.

At Zokyo, we bring deep expertise in both traditional security and emerging AI threats. Our team helps organizations identify, assess, and remediate vulnerabilities across the full AI stack, from training data integrity to deployment security. If you are building with LLMs, securing them should not be an afterthought.