OWASP Top 10 for LLM Applications 2026: What Changed and What It Means for Your AI Security

The OWASP Top 10 for LLM Applications 2026 reflects where AI deployments have moved. Agents now take actions, connect to tools, and operate with consequences that security controls have not kept pace with. 

The incidents from the past year make the risk concrete. The Gemini Trifecta exposed prompt injection vulnerabilities across Google’s Cloud Assist, Search, and Browsing tools, leading to private data exfiltration. Perplexity’s Comet AI browser let attackers steal emails and banking credentials through ordinary web pages.  

Each of these incidents maps to one or more risks in the 2026 list. The table below shows how the rankings changed from 2025, and the sections that follow explain all 10 risks in detail. 

OWASP Top 10 LLM Application 2026 

What changed: 2025 to 2026 (New Section) 

Risk	2025 Rank	2026 Rank	What Changed
Prompt Injection	#1	#1	Held at top by expert opinion despite fewer public incidents
Sensitive Information Disclosure	#2	#2	Unchanged
Excessive Agency	#6	#3	Biggest mover — agentic AI deployments now mainstream
Supply Chain	#3	#4	Slight drop
Data and Model Poisoning	#4	#5	Slight drop
Unbounded Consumption	#10	#6	Rose sharply — resource abuse now broader than DDoS
Misinformation	#9	#7	Rose as AI output is trusted in more decisions
Hidden Context Exposure	#7	#8	Renamed from System Prompt Leakage, broader scope
Vector and Embedding Weaknesses	#8	#9	Slight drop
Improper Output Handling	#5	#10	Dropped significantly

The two changes that matter most for security teams: 

Excessive Agency at #3 is the clearest signal of where the 2026 edition differs from 2025. When AI was primarily chatbots, excessive agency was a concern in theory. Now that agents connect to file systems, databases, and APIs and act autonomously, a compromised agent carries a far larger blast radius than a chatbot ever did.  

Hidden Context Exposure replacing System Prompt Leakage is a scope expansion. The 2025 category was about system prompts being leaked. The 2026 category covers any non-user-facing context an attacker can read back: memory, retrieved documents, tool outputs, conversation history. As AI systems accumulate more context over time, what they can expose grows with it. 

LLM01:2026 Prompt Injection  

Prompt injection holds the top position across every OWASP LLM edition since 2023.  prompt injection exploits how large language models (LLMs) process input prompts, enabling attackers to manipulate the model’s behavior or outputs in unintended ways. While methods such as Retrieval Augmented Generation (RAG) and fine-tuning are designed to enhance the relevance and precision of LLM outputs, they are not sufficient to fully address prompt injection vulnerabilities. 

Why it still tops the list in 2026: 

In June 2025, EchoLeak (CVE-2025-32711, CVSS 9.3) demonstrated zero-click indirect prompt injection against Microsoft 365 Copilot. A single crafted email exfiltrated internal file contents without the user doing anything beyond receiving it. Microsoft patched the specific instance, but the vulnerability class remains open across any LLM that processes external content. 

73% of production AI deployments assessed in security audits show prompt injection exposure, according to OWASP. Only 34.7% of organizations have deployed dedicated defenses. 

Types of Prompt Injection: 

Direct Injection (Jailbreaking) 
The attacker supplies the malicious instruction directly. The goal is to override system prompt constraints or extract information the model was instructed not to share. 

Indirect Injection 
Instructions are hidden inside content the model processes: a webpage, a document, an email, a database record. The model reads the content and acts on the embedded instruction without the user realizing. This is how EchoLeak worked. 

Payload Splitting 
The malicious instruction is split across multiple inputs that are harmless individually but reconstruct the attack when processed together. 

What makes prompt injection worse in agentic deployments: 

When an LLM is a chatbot, a successful prompt injection gets the attacker a response they were not supposed to receive. When an LLM is an agent connected to tools, a successful prompt injection gets the attacker an action: a file read, a credential exfiltrated, an email sent, a database queried. The same attack that was an information disclosure against a chat interface becomes a full compromise against an agent. 

How to architect against prompt injection: 

• Separate external content from instructions. Documents, emails, web pages, and RAG-retrieved content should never be able to override system behavior or influence tool calls without explicit validation. The model cannot reliably distinguish content from commands. The architecture must make that distinction instead. 
• Apply least privilege. The blast radius of a successful prompt injection is directly proportional to what the model can reach. Scope access at the task level and revisit those scopes when use cases expand. 
• Require human approval for high-stakes actions. Any action that cannot be reversed should require explicit human confirmation before execution. Administrative actions, external data transfers, and account changes should not run automatically. 
• Test with adversarial inputs regularly. Prompt injection testing should be part of your security evaluation cycle, not a one-time exercise. The attack techniques evolve with the models. 
• Monitor what the model accesses and returns. Log retrieval activity, flag anomalous access patterns, and track what data surfaces in model responses. 

LLM02:2026 Sensitive Information Disclosure   

LLMs embedded in applications can expose sensitive data, proprietary algorithms, or confidential details through their outputs. The risk has not changed in rank, but the attack surface has grown. In 2026, AI systems process more internal data than ever. RAG pipelines pull from internal knowledge bases, agents retrieve files and database records, and memory systems accumulate context across sessions. Every additional data source the model can access is a potential disclosure path. 

The mitigation is controlling what the model can see and what it can return. Sanitize data before it enters the model’s context. Apply output filtering to catch sensitive content before it reaches the user. Never treat the model’s access controls as a substitute for data-layer controls. 

LLM03:2026 Excessive Agency  

Excessive Agency moved from #6 in 2025 to #3 in 2026. This is the most significant ranking change in the 2026 edition, and it reflects what has changed in production: AI systems are no longer just answering questions. They are connected to tools that let them take real actions. 

An AI agent with access to your email, calendar, CRM, and file system is a powerful productivity tool. It is also a significant attack surface. If that agent can be manipulated through prompt injection or if it has more permissions than it needs for its task, an attacker has a pivot point into every system the agent can reach. 

83% of organizations plan to deploy agentic AI, but only 29% feel ready to secure it, according to Cisco’s State of AI Security 2026 report. That gap is exactly what OWASP is flagging by moving this risk to #3. 

How to limit what agents can actually do: 

• Apply the principle of least agency. Every agent should operate with only the permissions required for its declared task. An email summarization agent should not have send permissions. A document retrieval agent should not be able to delete files. Scope tool access at the task level. 
• Limit what each tool exposes by default. Sensitive operations such as account changes, external data transfers, and administrative actions should require explicit validation or human approval before execution. 
• Monitor how agents chain tools together at runtime. Risky behavior tends to appear in sequences of actions. Visibility into tool-level activity is what surfaces misuse before it compounds. 

LLM04:2026 Supply Chain  

LLM supply chain attacks target the components organizations rely on to build and run AI systems: training datasets, pre-trained models, fine-tuning pipelines, third-party plugins, and model hosting platforms. A compromised component can introduce backdoors, biased behavior, or malicious code that is invisible in the final application. 

The Cline/OpenClaw supply chain attack in February 2026 is the clearest recent example. Prompt injection in a Claude-powered GitHub Actions workflow led to a compromised npm package. Once installed on approximately 4,000 developer machines, it exposed credentials, SSH keys, and cloud tokens.

How to reduce implicit trust across the LLM supply chain: 

• Maintain a cryptographically signed Software Bill of Materials for every AI component. Source models and adapters only from verified platforms with integrity checks. 
• Run security testing on fine-tuned models before deployment. A fine-tuned model inherits the base model’s capabilities and adds its own attack surface. 
• Implement supply chain kill switches. When a compromise is detected in a plugin or external component, the ability to disable it across all deployments immediately matters more than any individual firewall rule. 

Deep dive into Supply Chain Attacks and prevention. 

LLM05:2026 Data And Model Poisoning  

Data poisoning attacks introduce malicious data into training, fine-tuning, or embedding pipelines to manipulate model behavior. A poisoned model may produce biased outputs, contain hidden backdoors that activate on specific inputs, or quietly degrade performance in ways that are difficult to detect. 

The risk is elevated in 2026 because organizations are increasingly fine-tuning models on proprietary data, the same data an attacker could target to shape the model’s behavior in production.  

How to protect the training and fine-tuning pipeline: 

• Validate datasets at every stage of the pipeline. Do not assume that data sourced from internal systems is automatically trustworthy. Apply anomaly detection to identify adversarial inputs before they enter the training or fine-tuning process. 
• Apply data version control so that changes to training data are auditable and reversible. When a poisoned model is discovered, tracing which data introduced the vulnerability requires a complete change history. 
• Run red team testing against fine-tuned models. Evaluate model behavior under adversarial inputs before deployment and after major data updates. 

LLM06:2026 Unbounded Consumption 

Unbounded Consumption moved from the bottom of the list to #6. In 2025, this was primarily about DDoS-style attacks overwhelming LLM infrastructure. In 2026, the scope has expanded to include resource exhaustion through legitimate-looking usage, Denial of Wallet attacks against pay-per-use cloud AI services, and systematic model extraction through crafted queries. An attacker who can drive up consumption can cause financial damage without any data breach. 

How to prevent resource exhaustion and model extraction: 

• Implement rate limiting per user, per API key, and per endpoint. Set hard limits on input length and output generation. Neither should be unbounded by default. 
• Monitor token consumption anomalies as an attack signal, not just an operational metric. A sharp or sustained increase in consumption from a single source is worth investigating before it becomes a billing problem. 
• Restrict output of logits and logprobs in API responses. Detailed model internals exposed through API responses reduce the effort required for model extraction attacks. 

LLM07:2026 Misinformation  

Misinformation rose in rank as AI output is trusted in more consequential decisions. The risk is the model generating plausible but incorrect content that users act on without verification. Air Canada’s chatbot misinformation led to legal liability. LLMs fabricating legal citations has affected court proceedings. In clinical and financial contexts, a confidently wrong AI output carries real consequences. 

How to reduce the downstream impact of misinformation: 

• Ground model outputs in verified external sources through RAG where accuracy matters. A model that retrieves from trusted, current sources produces fewer fabrications than one relying on training data alone. 
• Build verification steps into workflows before AI outputs drive decisions. Consequential decisions should not route through an LLM without an independent review step. 
• Communicate the model’s limitations to users, especially in high-stakes contexts. Interfaces should surface confidence levels and source provenance rather than presenting outputs as authoritative. 

LLM08:2026 Hidden Context  

In 2025 this was System Prompt Leakage. In 2026 it is Hidden Context Exposure, with a scope that goes beyond system prompts to cover any context the model holds that was not intended to be visible to the user: retrieved documents from RAG systems, tool outputs, memory contents, conversation history, and embedded credentials. 

In 2025, the concern was primarily that a system prompt containing credentials or sensitive business logic could be extracted. In 2026, AI systems hold significantly more context. They retrieve documents, remember past interactions, and pull tool outputs into their working memory. All of it is potentially extractable through the same prompt manipulation techniques. 

How to prevent hidden context from becoming a leak path: 

• Never embed sensitive data in system prompts. Credentials, database connection strings, and internal business rules should not live inside the prompt. If it is in the prompt, it can be extracted. 
• Use external controls to enforce model behavior. Security checks must be independent of the LLM. 
• Treat all non-user-facing context as sensitive. Apply the same access controls to RAG-retrieved documents, memory contents, and tool outputs that you would apply to the underlying data source. 

LLM09:2026 Vector and Embedding Weaknesses 

Vector and embedding weaknesses affect RAG systems and other retrieval architectures. Vulnerabilities in how embeddings are created, stored, and retrieved can expose sensitive data, allow cross-context information leaks, or enable embedding inversion attacks that reverse embeddings to extract confidential data. In 2026, RAG powers production customer service systems, internal knowledge assistants, and compliance tools. The data these systems retrieve is often sensitive, and access controls around vector databases frequently lag behind the sensitivity of the data they hold. 

How to secure the retrieval and embedding layer: 

• Enforce access controls at the retrieval layer. A user who cannot access a document directly should not be able to retrieve it through a vector search. 
• Log all retrieval activity. Immutable logs of what was retrieved, when, and by whom are necessary for both anomaly detection and post-incident investigation. 
• Monitor for anomalous access patterns. Repeated queries targeting the same embeddings, unusual retrieval volumes, or cross-tenant access patterns are signals worth alerting on. 

LLM10:2026 Improper Output Handling  

Improper Output Handling dropped from #5 to #10, the largest downward move in the 2026 list. When LLM output is passed to downstream systems without validation or sanitization, it can trigger cross-site scripting, SQL injection, or command execution depending on how the output is used. 

How to treat LLM output as untrusted input: 

• Validate and sanitize everything the model returns before it reaches another system. LLM output is not trusted content. Apply the same validation discipline you would to any third-party API response. 
• Apply context-appropriate encoding. HTML encoding for web content, SQL escaping for database operations, and shell escaping for command execution. The encoding must match the context where the output lands. 
• Use parameterized queries to prevent SQL injection. Any database operation that involves LLM output must use parameterized queries. Never interpolate model output directly into a SQL statement. 
• Enforce Content Security Policies for any output rendered in a browser. This limits the damage if malicious content reaches the front end despite output filtering. 

How AppTrana AI Shield Protects Against Prompt Injection 

AppTrana AI Shield is an AI firewall deployed inline between users and your LLM, inspecting every prompt and response in real time. It detects and blocks direct injection, indirect injection, jailbreaks including obfuscation techniques, and sensitive data leakage before it reaches the user or leaves the application. 

Coverage maps to the OWASP LLM Top 10 2026 across every major risk category. For prompt injection specifically, AI Shield detects structural attack patterns behaviorally, catching novel variations and obfuscated payloads that signature-based tools miss. 

When a new threat pattern is identified, AppTrana’s autonomous remediation deploys application-specific protection rules at the edge, tested against live traffic before enforcement. The window between detection and protection closes in hours, not sprint cycles. 

Bot protection blocks automated prompt flooding and systematic knowledge base scraping. Indusface security experts tune and monitor policies around the clock.  

Start a free trial and see how AppTrana AI Shield protects your LLM applications against OWASP Top 10 threats.