Understanding LLM Application Security Vulnerabilities: An OWASP Perspective

LLM application security vulnerabilities are critical concerns for modern AI-powered systems, extending beyond traditional web application threats. These vulnerabilities, often highlighted by frameworks like the OWASP Top 10 for LLM Applications, involve unique attack vectors targeting the model’s reasoning and processing capabilities. Understanding and mitigating these risks, such as prompt injection and data poisoning, is essential for securing LLM-based applications and preventing unauthorized access, data breaches, or system compromise. Pentrova offers exploit-verified coverage for LLM, RAG, and agent-driven stacks, providing automated penetration testing with replay-verified exploits to confirm these vulnerabilities.

What are the OWASP Top 10 LLM Application Security Vulnerabilities?

The OWASP Top 10 for Large Language Model Applications identifies the most critical security risks specific to LLMs [1]. While versions may vary (e.g., 2023 vs. 2025), common themes persist. Key vulnerabilities include Prompt Injection, where crafted inputs manipulate LLM behavior; Insecure Output Handling, where unsafe validation of outputs can cause downstream exploits; and Training Data Poisoning, where tampered data degrades model integrity [2, 3, 4]. Other significant risks encompass Model Denial of Service, Supply Chain Vulnerabilities, and Sensitive Information Disclosure [2, 3]. These vulnerabilities reflect attacks that target the LLM’s reasoning and processing rather than traditional web application threats like SQL injection or [4]. Pentrova’s AI penetration testing platform provides exploit-verified coverage for LLM, RAG, and agent-driven stacks, ensuring that these novel threats are identified and validated with deterministic proof. For more details on how Pentrova secures AI-native startups, visit our AI-Native Startups solution page.

Prompt Injection and System Prompt Leakage

Prompt Injection (LLM01:2023, LLM01:2025) is a primary LLM application security vulnerability where user prompts alter the model’s behavior or output in unintended ways [2, 3]. These inputs can affect the model even if they are imperceptible to humans, meaning prompt injections do not need to be human-visible as long as the content is parsed by the model [3]. A related vulnerability, System Prompt Leakage (LLM07:2025), involves the exposure of hidden instructions or system prompts that can be exploited to alter the model’s behavior or outputs [3]. Successful prompt injection can lead to unauthorized access, data breaches, or compromised decision-making [2]. Pentrova’s automated penetration testing platform is designed to identify and verify such logic exploits, providing deterministic PoCs and reproducible steps for developers to address these issues effectively. Learn more about our approach to automated penetration testing.

Data and Model Poisoning Risks

Data and Model Poisoning (LLM04:2025) represent critical LLM application security vulnerabilities, occurring when pre-training, fine-tuning, or embedding data is manipulated to introduce vulnerabilities, backdoors, or biases [3]. This manipulation can compromise model security, performance, or ethical behavior, leading to harmful outputs or impaired capabilities [3]. Training Data Poisoning (LLM03:2023) specifically refers to tampered data that degrades model integrity, accuracy, or ethics [2]. Such attacks can be launched by internal or external actors, aiming to influence model results or support AI-driven decision-making [4]. Mitigating these risks requires robust data provenance and integrity checks throughout the LLM lifecycle. Pentrova’s platform helps identify the downstream impacts of such vulnerabilities by providing replay-verified exploits, ensuring that even subtle data manipulations are detected and proven.

Improper Output Handling and Excessive Agency

Improper Output Handling (LLM02:2023, LLM05:2025) is a significant LLM application security vulnerability where insufficient validation, sanitization, and handling of LLM-generated outputs occur before they are passed downstream to other components and systems [2, 3]. Since LLM-generated content can be controlled by prompt input, this can provide users indirect access to additional functionality [3]. Successful exploitation can result in , , , privilege escalation, or remote code execution on backend systems [3, 4]. Excessive Agency (LLM08:2023, LLM06:2025) refers to the vulnerability where LLMs are granted unchecked autonomy to take actions, potentially leading to unintended consequences that jeopardize reliability, privacy, or trust [2, 3]. This often involves LLMs with access to sensitive APIs being persuaded to use them unsafely [5]. Pentrova’s automated web and API penetration testing capabilities include robust detection for insecure output handling and the potential for excessive agency, providing replay-verified evidence of exploitation. Explore our web application pentesting and API pentesting solutions.

LLM Supply Chain Vulnerabilities

LLM Supply Chain Vulnerabilities (LLM05:2023, LLM03:2025) are a growing concern, as LLM supply chains are susceptible to various risks that can affect the integrity of training data, models, and deployment platforms [2, 3]. These vulnerabilities extend beyond traditional software supply chain issues, encompassing third-party pre-trained models and data sources that can be manipulated through tampering or poisoning attacks [3]. Compromised components, services, or datasets can undermine system integrity, leading to data breaches and system failures [2]. The hidden attack surface of LLM-powered applications exists across interconnected layers, where indirect manipulation of the model through misleading or adversarial context can affect behavior without altering code [6]. Pentrova helps organizations address these complex risks by providing continuous penetration testing, offering a board-ready view of real, exploitable risk across every application, API, and integration. This ensures that all components, including those within the LLM supply chain, are continuously monitored for vulnerabilities.

FAQ

What are the primary types of LLM application security vulnerabilities?

The primary types include prompt injection, sensitive information disclosure, data and model poisoning, improper output handling, excessive agency, and supply chain vulnerabilities, as outlined by the OWASP Top 10 for LLM Applications [3].

How do prompt injection attacks work?

Prompt injection attacks involve crafting inputs that manipulate an LLM’s behavior or output, causing it to deviate from its intended purpose, potentially leading to unauthorized actions or data exposure [2, 5].

What is the risk of improper output handling in LLM applications?

Improper output handling can lead to downstream security exploits, such as cross-site scripting (), cross-site request forgery (), server-side request forgery (), or remote code execution (RCE), if LLM-generated content is not properly validated and sanitized [3, 4].

Why are LLM supply chain vulnerabilities a concern?

LLM supply chain vulnerabilities are a concern because compromised components, such as pre-trained models or training datasets, can introduce biases, backdoors, or security flaws that affect the integrity and security of the entire LLM application [3].

How does Pentrova address LLM application security vulnerabilities?

Pentrova’s AI-powered platform provides automated penetration testing with exploit-verified coverage for LLM, RAG, and agent-driven stacks. It identifies and validates vulnerabilities with replay-verified exploits, offering deterministic proof and reproducible steps for remediation [7].

What is ‘Excessive Agency’ in LLM security?

Excessive agency refers to an LLM-based system being granted too much autonomy, allowing it to perform dangerous actions beyond its intended scope, often by misusing access to sensitive APIs [3, 5].