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The Open Worldwide Application Security Project (OWASP®) has released the much-anticipated 2025 update to its OWASP LLM Top 10, providing critical guidance for securing large language model (LLM) applications. With generative AI (GenAI) and LLM systems transforming industries, this resource addresses evolving risks, offers practical mitigations, and anchors security efforts in real-world incidents and research. Here's an in-depth look at what this update offers and why it’s pivotal for developers, researchers, and enterprises working with LLMs.
The OWASP LLM Top 10 is a community-driven initiative designed to identify and address the top security risks associated with large language models. This list is essential for developers and organizations integrating AI into their workflows, as it highlights potential vulnerabilities and suggests actionable mitigation strategies.
With the explosion of Retrieval-Augmented Generation (RAG), agentic architectures, and complex prompt engineering, the 2025 update ensures the LLM security landscape is ready for what lies ahead.
The updated risks reflect a global effort, incorporating insights from researchers, practitioners, and real-world deployments. This ensures a holistic perspective on emerging LLM vulnerabilities.
RAG, which combines LLMs with external data retrieval systems, introduces unique attack vectors such as poisoning retrieved datasets or exploiting weak retrieval pipelines. OWASP’s update provides concrete recommendations to secure these pipelines and ensure robust integration.
Prompt engineering remains a cornerstone of LLM interaction, but it is fraught with risks:
Mitigation strategies in this update focus on robust prompt validation, sanitization, and contextual compartmentalization to minimize exposure.
Agentic AI systems, which autonomously execute tasks or interact with external environments, introduce a higher degree of complexity and risk. These systems are susceptible to:
OWASP emphasizes limiting agent permissions and implementing real-time monitoring of agent behavior to prevent misuse.
The update includes an extensive library of references, covering academic research, publications, and case studies. The risks are also mapped to MITRE’s Adversarial Tactics, Techniques, and Common Knowledge (ATLAS), enabling practitioners to leverage existing frameworks for threat modeling and mitigation.
Risk: Attackers manipulate user inputs to control the model's behavior, potentially generating malicious outputs or bypassing restrictions.
Example: Instructing an LLM to ignore safety filters and execute harmful commands by crafting misleading prompts.
Mitigation:
Risk: LLMs may inadvertently expose confidential data embedded in training sets or shared during interactions.
Example: Users extracting sensitive internal information (e.g., API keys or credentials) through crafted queries.
Mitigation:
Risk: Third-party dependencies or pre-trained models may introduce vulnerabilities into LLM applications.
Example: Incorporating a compromised pre-trained model with backdoors or malicious code.
Mitigation:
Risk: Malicious actors manipulate training or fine-tuning data to bias the model or introduce harmful behavior.
Example: Poisoning a training set to skew LLM responses or embed specific attack patterns.
Mitigation:
Risk: Unfiltered model outputs may include harmful, biased, or misleading information.
Example: LLMs providing offensive or inaccurate content due to unregulated post-processing.
Mitigation:
Risk: Agentic LLMs—systems with autonomous decision-making capabilities—can perform unintended or harmful actions.
Example: An LLM autonomously executing financial transactions without proper safeguards.
Mitigation:
Risk: Attackers uncover embedded system prompts, exposing sensitive configurations or enabling bypasses.
Example: Extracting hidden prompts that govern LLM behavior to manipulate the system.
Mitigation:
Risk: Exploiting weaknesses in vector representations or embeddings used for understanding context or retrieval.
Example: Adversarial inputs designed to cause incorrect interpretations or retrieval results.
Mitigation:
Risk: LLMs generating or amplifying false or misleading information, eroding user trust and causing harm.
Example: An LLM providing inaccurate medical advice or biased opinions.
Mitigation:
Risk: Excessive resource usage, such as high API calls or memory consumption, leading to denial of service or system degradation.
Example: A malicious user flooding the system with requests to overload resources.
Mitigation:
This list provides actionable guidance to build secure LLM applications, integrating mitigations from the design phase.
With LLMs increasingly handling critical business functions, addressing these risks minimizes legal, financial, and reputational damage.
The list serves as a foundation for studying and addressing LLM vulnerabilities, fostering collaboration between academia and industry.
The 2025 OWASP LLM Top 10 reflects the evolving challenges in securing LLM applications amidst rapid technological advancements. By understanding and addressing these risks, stakeholders can harness the full potential of LLMs while maintaining robust security and trust.
As generative AI shapes the future, proactive measures will ensure that this revolution remains both innovative and secure. Are you prepared to safeguard the next wave of AI systems?
SecOps Solution is a Full-stack Patch and Vulnerability Management Platform that helps organizations identify, prioritize, and remediate security vulnerabilities and misconfigurations in seconds.
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