The OWASP Top 10 for Agentic Applications 2026 identifies critical security vulnerabilities in autonomous AI systems. These systems operate by planning, deciding, and acting on multiple tasks through complex orchestration and multi-step autonomy. Threats arise from agent interaction, extended supply chains, persistent memory, and the potential to manipulate inputs and actions. Protection requires specific controls tailored to the autonomous nature of agents and their distributed integration.

The Top 10 vulnerabilities according to OWASP

ASI01: Agent Goal Hijack

Attackers manipulate an agent’s goals, tasks, or decisions through indirect prompt injection, deceptive tool outputs, poisoned documents, malicious artifacts, or manipulated external data. The agent, unable to distinguish legitimate instructions from toxic natural language content, may deviate from its original goals, causing data exfiltration, fraudulent financial actions, goal overrides via email and documents, or the production of false information.

Mitigations: Treat all input as untrusted, validate with anti-prompt injection mechanisms, apply least privilege to tools, require confirmation for high-impact actions, audit goal changes, perform runtime intent validation, sanitize data sources, implement continuous logging and monitoring, and conduct red team testing on goal override.

ASI02: Tool Misuse & Exploitation

Agents may use legitimate tools in malicious ways due to prompt injection, misalignment, or poor validation. This includes deletion of critical data, repeated and expensive calls, invocation of malicious shells, data poisoning via external content, and excessive use of privileges assigned to tools.

Mitigations: Least privilege for tools, sandboxing, authentication on every action, policy enforcement, management of ephemeral credentials tied to the session, semantic validation, continuous auditing of all tool actions, and immutable logs.

ASI03: Identity & Privilege Abuse

Privilege inheritance and delegation can lead agents to use credentials to perform unauthorized actions. This exploits gaps between identity systems and agentic design (authentication contexts, caching, cross-agent trust). Risks include role abuse via delegation chains, key retention in memory, and phishing between agents.

Mitigations: Session sandboxing, limiting the duration and scope of credentials, isolating identities per agent, centralizing authorizations and approvals for privileged steps, binding intents to authorizations, and detecting anomalous escalation or device-code phishing on agents.

ASI04: Agentic Supply Chain Vulnerabilities

Agents can be exposed to dynamically loaded and potentially malicious or manipulated external components, tools, models, or registries. Risks such as poisoned prompt templates, injection in tool metadata, agent impersonation, or typo-squatting increase the attack surface.

Mitigations: Sign and attest every component via SBOM/AIBOM, sandbox agents, implement mutual authentication between peers, continuous validation, content pinning, and a kill switch mechanism for emergency revocation.

ASI05: Unexpected Code Execution (RCE)

Agents that generate or execute code are exposed to exploits due to prompt injection, unsafe deserialization, the use of insecure eval functions, the installation of malicious packages, and unvalidated shell commands. Scenarios include unintended code execution and persistent compromise of the host machine.

Mitigations: Prohibit eval in production, sandbox code execution, enforce minimal privileges, perform static analysis of generated output, require human approval for critical actions, and implement dynamic analysis and blocklists for suspicious packages.

ASI06: Memory & Context Poisoning

Persistent agentic memory (vector databases, sessions, RAG stores, summaries, shared context) can be contaminated by false or manipulated data. This alters future decisions, reasoning, or tool selection, leading to systemic errors and data leaks between users or sessions.

Mitigations: Encryption and segmentation of memory, validation and provenance of information, context isolation, minimizing retention, rolling back on anomalies, decaying unverified memories, and blocking the automatic re-insertion of self-generated outputs.

ASI07: Insecure Inter-Agent Communication

Unauthenticated or poorly integrated communication between agents exposes systems to replay attacks, man-in-the-middle, spoofing, tampering, schema forgery, and metadata inference. Lacking strong authentication and encrypted channels, agents can assume malicious roles and goals, propagating attacks throughout the network.

Mitigations: End-to-end encryption with per-agent credentials, digital signatures on messages, anti-replay mechanisms, authenticated discovery and routing, versioning policies and disabling of weak protocols, and verification of agent descriptors and capabilities.

ASI08: Cascading Failures

A single error (hallucination, memory poisoning, tool compromise) amplifies as it propagates between agents, tools, and workflows, causing systemic impacts on confidentiality, integrity, and availability. Risks include oscillating retries, feedback loops, and escalation of damage without human control.

Mitigations: Zero-trust design and resilience, external policy enforcement, checkpoints and human reviews where necessary, segmentation, just-in-time credentials, rate limiting, ex-post simulations for auditing and policy gating, and tamper-evident logs.

ASI09: Human-Agent Trust Exploitation

The natural human propensity to trust autonomous agents is exploited through deceptive explanations, emotional manipulation, and perceived authority. Dangerous actions are thus approved by the user, bypassing security controls.

Mitigations: Multi-step confirmations, immutable logs, behavioral detection, anomaly reporting, adaptive trust calibration, enforcement of data provenance, separation of preview/action, visual cues, and anti-manipulation training.

ASI10: Rogue Agents

Compromised agents deviate and act autonomously in a malicious or collusive manner, exploiting control gaps to exfiltrate data, orchestrate illicit workflows, self-replicate, or sabotage systems. Emergent behavior becomes harmful and difficult to contain.

Mitigations: Immutable and signed audits, trust zones and sandboxes, behavioral monitoring, containment and rapid revocation, signed and verified behavioral manifests, and reintegration only after verification and human approval.

Cross-cutting mitigations and best practices

• Always apply least privilege and least agency: reduce unnecessary autonomy and privileges for agents and tools.
• Sanitize and validate any input (prompts, tools, data, documents, communication channels).
• Use sandboxes and policy enforcement at every level of action and communication between agents.
• Implement exhaustive logging, traceability, anomaly alerting, and periodic testing (red teaming and digital twin replay).
• Integrate kill switches for immediate revocation, staged rollouts, dependency resilience, and supply chain governance.
• Provide for human-in-the-loop for critical and out-of-policy actions, continuous training, and feedback on trust abuse.

References and further reading

• OWASP Top 10 for Agentic Applications 2026 – Official document from the OWASP GenAI Security project
• ASI Agentic Exploits & Incidents – GitHub repository with updated incidents and use cases

The security of agentic systems requires mitigations aimed at limiting autonomy, isolating contexts, validating every channel and action, monitoring signs of deviance, and reacting rapidly through auditing and control tools.