The OWASP Top 10 for LLM Applications 2026: From Model Risks to Agentic Security

One of the clearest themes in the 2026 list is the growing importance of agentic AI.

AI applications are no longer limited to generating text. Modern AI models interact directly with enterprise systems. They can retrieve data, invoke tools via APIs, and execute high-impact business workflows. 

This shift is reflected especially in LLM03: Excessive Agency and LLM08: Hidden Context Exposure, both of which receive greater prominence in the 2026 edition.

• LLM03 addresses the risks created when an AI system has excessive functionality, permissions, or autonomy. A manipulated or unreliable model becomes significantly more dangerous when it can access sensitive or private information, update records, execute workflows, or trigger external systems without sufficient segmentation.

• LLM08 expands the previous focus on system prompt leakage. Hidden context may now include developer instructions, internal configurations, retrieved policies, application workflows, user roles, tool and function schemas, permission models, and other operational information assembled inside the model’s context window.

Together, these changes reflect the growing security impact of AI systems that can both access internal context and act on it. As AI applications gain deeper access to enterprise data, tools, and workflows, weaknesses that once affected only model output can increasingly translate into real actions and broader system-level impact.

The increased prominence of LLM03 also reflects a broader trend across the industry. More AI applications are being equipped with connected tools and the ability to invoke them dynamically according to user requests or model-generated decisions.

These capabilities allow AI systems to move beyond generating answers. They can retrieve information from connected systems, access enterprise data, update records, execute workflows, call external services, and perform actions on behalf of users.

This is clearly where the industry is heading, but it also creates a much broader attack surface. Security breaches now extend beyond misleading text outputs. Attackers can exploit authorized tools, bypass workflows, exfiltrate sensitive data, or trigger unauthorized actions. 

By examining interactions across users, models, tools, and connected systems, Akamai Firewall for AI helps organizations maintain visibility and control as AI applications gain more autonomy and operational capabilities.

LLM08 is particularly notable because it formalizes a risk that we have already identified through our AI security research: AI reconnaissance.

Attackers do not always begin with an obvious request for sensitive data or with an immediate attempt to exploit a tool. Instead, they may first try to map the application:

• What is the assistant capable of doing?

• Does it use a knowledge base?

• Which tools and integrations are available?

• What permissions and roles exist?

• Which requests will it refuse?

• How are workflows and decisions structured?

Individually, these questions may appear harmless. Together, their answers can provide the intelligence needed to create a precise, application-specific attack (as we demonstrated in a previous blog post).

The Hidden Context Exposure category (LLM08) captures this risk through the disclosure of tool schemas, behavioral logic, permissions, roles, refusal mechanisms, and workflow rules. This information can then support more targeted prompt injection, unauthorized access, tool abuse, or malicious action chaining.

This closely matches what we observed in our research across more than 170 real-world AI assistants. An assistant may resist a direct request for sensitive information while still revealing details about its capabilities, operational boundaries, connected tools, or knowledge sources. That information can convert a generic attack into a highly targeted threat.

We recognized early that protecting only against the final exploit leaves an important gap. By the time an attacker submits a highly targeted prompt injection or malicious tool request, they may already have gathered the context needed to bypass application-specific controls.

For this reason, Akamai Firewall for AI customers will soon have access to a dedicated and unique reconnaissance detector that is designed to identify probing behavior such as:

• Capability and boundary discovery

• Tool and function enumeration

• Knowledge-base probing

• Permission and role discovery

• Attempts to reconstruct internal instructions or workflow logic

This will enable organizations to detect suspicious activity at the beginning of the attack chain, before the attacker progresses to prompt injection, unauthorized data access, workflow abuse, tool misuse, or malicious actions.

Firewall for AI operates inline with AI application traffic and evaluates both user prompts and model responses. Organizations can apply application-specific policies and choose whether detected activity should be monitored, modified, or denied, regardless of the underlying model provider or AI platform.

The OWASP Top 10 for LLM Applications 2026 does not completely redefine AI security. Instead, it provides a more mature and evidence-based view of the risks already emerging in production.

Securing agentic AI requires visibility across the entire attack chain - from reconnaissance and context discovery to data exfiltration or unauthorized actions.

Firewall for AI is built for this reality. It provides runtime protection across interactions with AI applications, helping organizations identify and control threats as users interact with models, and as those models become connected to sensitive data, tools, and business workflows.

Firewall for AI protects a broad range of AI-specific risks, including prompt injection and jailbreak attempts, sensitive data exposure, reconnaissance activity, harmful or off-topic use, and emerging agentic threats. 

By applying security controls directly to AI traffic, organizations can detect suspicious behavior, enforce policies before risky interactions progress further, and gain visibility into how their AI applications are actually being used and attacked. 

This becomes increasingly important as AI systems evolve from standalone chat interfaces into applications that can retrieve internal information, maintain context, invoke tools, and perform actions on behalf of users. 

Additionally, Akamai offers comprehensive AI security solutions that help enterprises operate safely in an AI-powered environment and secures digital interactions, both inside and outside an organization. Some of the key AI security solutions include Akamai Guardicore Segmentation, Akamai Workforce Protector, and Akamai Application Protection Platform, among others.

Talk to your Akamai representative today to learn more about Akamai’s AI security solutions.