Generative AI (GenAI) continues transforming industries, revolutionizing everything from customer service to software development. However, as organizations race to adopt Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) based systems, the security landscape is rapidly shifting.
Recognizing this, OWASP released its 2025 Top 10 GenAI Security Risks, reflecting the evolving threat landscape in real-world LLM deployments. This list addresses not just prompt injections or hallucinations but also embedding abuse, RAG exploitation, and prompt leakage, all unique to GenAI systems.
Modern enterprises are deeply integrating GenAI into operations, such as:
These systems can now make decisions, interact with APIs, and store dynamic memory, widening the attack surface far beyond traditional applications. Without clear boundaries or human review, security teams face a growing need to adapt threat models to GenAI contexts.
GenAI systems differ from traditional apps in key ways:
The OWASP GenAI Top 10 provides the foundational lens to effectively understand and mitigate these risks.
Attackers manipulate model behavior by injecting hostile content into user prompts or context. This remains the top risk.Mitigation: Use strict system prompts, escape user inputs, and apply prompt isolation.
LLMs may unintentionally expose secrets seen during training or via retrieval systems.Mitigation: Redact sensitive training data, apply differential privacy, and enforce access control.
Malicious models, plugins, datasets, or open-source tools may compromise your entire GenAI stack.Mitigation: Scan dependencies, verify sources, and restrict external plugin execution.
Adversaries tamper with training or fine-tuning data, inserting backdoors or toxic behavior.Mitigation: Audit data pipelines, monitor behavior, and use clean-label techniques.
LLMs can generate misleading, biased, or even malicious content, which may be consumed blindly.Mitigation: Sanitize all outputs, apply response validation, and avoid direct execution of outputs.
Systems are granting GenAI autonomous control over tasks like approvals or system changes – often without checks.Mitigation: Keep a human in the loop, especially for high-risk operations.
Attackers uncover hidden system instructions (e.g., jailbreaks), allowing them to override controls.Mitigation: Obfuscate system prompts, segment user and system instructions, and monitor prompt chain leakage.
RAG systems use vector databases for context. If these are poisoned or manipulated, attackers can inject malicious data.Mitigation: Monitor vector store access, validate indexed content, and authenticate embedding pipelines.
LLMs may confidently generate false or biased content. Overreliance can lead to reputational or operational damage.Mitigation: Flag low-confidence outputs, add citations in RAG, and encourage human review.
LLMs can be abused to cause resource exhaustion or cost explosion via complex prompts or infinite loops.Mitigation: Rate limit inputs, cap token usage, and monitor prompt cost metrics.
The GenAI revolution brought new capabilities and unprecedented risks. OWASP’s 2025 Top 10 list helps teams proactively secure LLM-based systems before attackers exploit them.As GenAI becomes more embedded and autonomous, security cannot be an afterthought, it must be a foundational layer.
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