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The rapid rise of large language models (LLMs) has been nothing short of revolutionary, reshaping industries and redefining how we interact with technology. Yet, with great potential comes significant risk. The inaugural OWASP Top 10 for LLM Applications 2025 shines a spotlight on the critical vulnerabilities developers and organizations must address to ensure their systems remain safe, trustworthy, and efficient.
If some of these risks feel familiar, that’s because they are. Many echo the security challenges developers have faced with web applications for years—think injection attacks and improper data handling. But with LLMs, the stakes are higher, and the solutions are more nuanced. LLMs don’t just process inputs; they interpret, generate, and act on them. Their dynamic interaction with users and their vast processing power introduce vulnerabilities that require fresh thinking.
OWASP’s list offers a lens—a structured way to evaluate how these AI systems can be safely built, deployed, and maintained in a world that increasingly relies on their capabilities. Whether you’re a developer, a decision-maker, or simply someone interested in the evolving landscape of AI security, this short guide walks you through the key risks and why they matter, drawing connections to longstanding practices while offering a clear view of what’s new along with mitigation techniques.
Prompt injection exploits the shared channel in LLMs where instructions and data overlap, allowing malicious users to embed harmful commands into inputs. Unlike traditional injection attacks, this vulnerability is unique to the interpretive nature of LLMs.
Why It Matters: Imagine a customer support bot manipulated into leaking sensitive account details or bypassing internal workflows. Such breaches erode trust and expose organizations to reputational, operational, and legal risks. As LLMs expand into high-stakes applications, prompt injection represents a critical, evolving threat.
Mitigation: According to the OWASP guide, mitigating prompt injection involves strict input validation, context isolation, and employing guardrails around user inputs to prevent commands from being misinterpreted.
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Sensitive information disclosure occurs when LLMs unintentionally reveal private, proprietary, or restricted data during interactions. This vulnerability can arise from poorly sanitized training datasets, insufficient filtering of outputs, or inadvertently exposed system prompts.
Why It Matters: Consider a customer-facing LLM unintentionally disclosing sensitive information like account credentials, personal identifiers, or confidential business strategies during a live chat. In sectors like healthcare, finance, or enterprise software, such incidents could result in severe regulatory penalties, legal challenges, and reputational harm. With increasing reliance on LLMs for critical applications, mitigating this risk is essential to safeguarding trust and compliance.
Mitigation: According to the OWASP guide, addressing sensitive information disclosure involves implementing output filters, sanitizing datasets, and enforcing access controls. These measures focus on minimizing the risk of both accidental leaks and targeted attacks.
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Supply chain vulnerabilities arise when third-party components, such as plugins, pre-trained models, or external APIs, introduce exploitable weaknesses into LLM systems. These components often extend functionality but can also serve as attack vectors if compromised.
Why It Matters: A single compromised plugin could act as a backdoor, exposing sensitive data, degrading system performance, or providing attackers with a foothold to infiltrate broader systems. With the growing reliance on expansive LLM ecosystems, including pre-trained models and integrations, the risk of supply chain attacks has become increasingly significant. Inadequate oversight of dependencies can jeopardize the security of the entire application.
Mitigation: The OWASP guide emphasizes robust dependency management, continuous monitoring, and secure integration practices to reduce the attack surface.
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Data and model poisoning occurs when attackers inject malicious, biased, or deceptive data into training datasets or during model updates. This manipulation can result in harmful outputs that distort the model’s behavior or undermine its reliability.
Why It Matters: Imagine an LLM used in financial decision-making being fed poisoned training data that skews its recommendations to favor harmful investments or fraudulent activities. The implications go beyond technical issues—regulatory scrutiny, legal liabilities, reputational damage, and financial losses can quickly follow. Worse, biased or poisoned models may perpetuate systemic harms, embedding misinformation or unethical practices into critical applications.
Mitigation: The OWASP guide underscores the importance of robust data pipelines, validation protocols, and secure update processes to mitigate poisoning risks.
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Improper output handling occurs when LLMs generate unfiltered, unchecked, or unsanitized responses. This oversight can result in outputs that are misleading, offensive, or inappropriate for their intended audience, creating significant reputational, operational, and legal risks.
Why It Matters: Consider a customer-facing chatbot generating offensive content or an AI-driven content generation tool publishing factually incorrect material. In industries like healthcare, finance, or media, such lapses could lead to user distrust, regulatory penalties, or widespread misinformation. Even seemingly minor errors can escalate into PR disasters, eroding confidence in the system and the organization behind it.
Mitigation: The OWASP guide highlights the need for rigorous post-processing and content moderation to address this challenge.
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Excessive agency refers to scenarios where LLMs operate autonomously, taking actions beyond their intended scope without adequate human oversight. This is especially prevalent in agentic architectures, where LLMs interact with other systems or plugins to execute complex workflows.
Why It Matters: Imagine an LLM-enabled assistant with the ability to autonomously perform actions like transferring funds, sending emails, or downloading files. If these capabilities are not properly constrained, unintended or malicious actions could result in financial loss, ethical violations, or significant security breaches. The increasing integration of LLMs into critical workflows magnifies the potential risks, making unchecked autonomy a critical concern for developers and organizations alike.
Mitigation: The OWASP guide emphasizes implementing strict access controls to limit the autonomy of LLMs.
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System prompt leakage occurs when the hidden system instructions embedded within prompts are unintentionally exposed, giving attackers insights into the model’s behavior and structure. These exposed instructions can be manipulated to alter or bypass the intended functionality of the system, creating a critical vulnerability.
Why It Matters: Hidden prompts often contain sensitive configurations, operational rules, or proprietary logic that govern how the LLM functions. If these prompts are leaked, attackers could exploit the information to manipulate the system or uncover vulnerabilities. For example, a leaked system prompt in a customer service chatbot could reveal proprietary algorithms, lead to unauthorized access, or even allow attackers to bypass authentication steps. As LLMs become central to many applications, safeguarding system prompts is vital to maintaining both security and operational integrity.
Mitigation: The OWASP guide highlights isolating and encrypting system prompts as key strategies.
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Vector and embedding weaknesses occur when attackers exploit vulnerabilities in the mathematical representations used by LLMs to process and retrieve context. These weaknesses are especially critical in systems relying on RAG (Retrieval-Augmented Generation) , where embeddings are used to fetch and ground model outputs in relevant information. A compromised embedding model can result in corrupted context, biased outputs, or the exposure of sensitive data.
Why It Matters: Embeddings are central to the effectiveness of LLMs in handling complex, context-driven queries. For example, a RAG-based customer support system might rely on embeddings to retrieve knowledge base entries. If an attacker manipulates the embeddings, the model might produce incorrect responses, leak sensitive data, or exhibit biased behavior. Beyond technical failures, this can erode user trust, damage brand reputation, and lead to costly operational failures.
Unlike traditional hashing or encryption, embeddings are not inherently secure and can be reverse-engineered to infer sensitive details. The growing reliance on RAG amplifies the potential impact of such vulnerabilities, making their mitigation a high priority.
Mitigation: The OWASP guide emphasizes robust vectorization practices and validation.
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Misinformation arises when LLMs generate outputs that appear credible but are factually incorrect or misleading. This issue is often rooted in biases within the training data, gaps in model understanding, or deliberate manipulation by bad actors. The challenge lies in the model’s ability to produce responses with high linguistic confidence, which can make falsehoods seem authoritative.
Why It Matters: The consequences of misinformation extend far beyond technical inaccuracies. Consider a healthcare chatbot dispensing incorrect medical advice or a financial news generator fabricating stock predictions—these scenarios can result in real-world harm, from public panic to financial losses and even life-threatening outcomes.
In industries like public health, education, and media, the stakes are particularly high. A single instance of misinformation can erode trust, damage reputations, and invite legal or regulatory scrutiny. As LLMs are increasingly integrated into decision-making processes, the ability to mitigate misinformation becomes not just a technical requirement but an ethical imperative.
Mitigation: The OWASP guide highlights the importance of validating outputs to prevent misinformation.
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Unbounded consumption occurs when an LLM consumes excessive resources, such as processing power, memory, or API calls, leading to operational inefficiencies. This vulnerability can stem from poorly managed resource allocation, unregulated user behavior, or unexpected system demands.
Why It Matters: Imagine a high-traffic application relying on an LLM—such as a customer support chatbot—suddenly experiencing a surge in user queries. Without proper limits, the system could exhaust available resources, leading to costly API overruns or outright service outages. For organizations, the consequences range from ballooning operational costs to lost revenue and diminished user trust.
In sectors like e-commerce, healthcare, or real-time analytics, where high availability is critical, resource overconsumption can disrupt user experiences, tarnish reputations, and even expose companies to regulatory risks if SLAs (Service Level Agreements) are violated.
Mitigation: The OWASP guide emphasizes the importance of resource management and limiting unbounded consumption.
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The OWASP Top 10 for LLM Applications provides a roadmap for navigating the unique security challenges posed by these transformative tools. While the risks are significant, proactive mitigation strategies can protect users, enhance trust, and ensure the long-term success of LLM systems.
At Sandgarden, we specialize in building secure, scalable AI solutions. Whether you’re tackling instruction injection or optimizing resource allocation, we’re here to help you navigate the complexities of LLM security. Learn more about how Sandgarden can help.
