OWASP Agentic AI Top 10 (2026): The New Security Baseline for Autonomous Systems

TL;DR: The OWASP GenAI Security Project’s Top 10 for Agentic Applications (2026) is the clearest baseline we have right now for securing autonomous agents. If your “AI” can take actions (call tools, run code, write to systems, or coordinate other agents), you need to secure it like a production system and like a privileged operator. Start with least agency, treat agents as non-human identities, constrain tools with allowlists, and instrument end-to-end observability.

If you’re deploying agents to automate back office work, customer ops, internal IT, or engineering workflows, this list is not “security theory.” It’s the practical set of failure modes that show up when an LLM stops being a chat interface and becomes an autonomous system.

Why agentic AI needs its own Top 10

Traditional AppSec assumes the software is deterministic: inputs flow through code, and the code decides what happens next. Agentic systems break that assumption:

• The “logic” is probabilistic: the model chooses actions based on context, not a fixed decision tree.
• The agent is a principal: it can hold credentials, call APIs, read/write data, and trigger workflows.
• The input surface is enormous: user prompts, emails, tickets, docs, web pages, code, and internal tools.
• The blast radius is nonlinear: one compromised step can cascade across tools, memory, and other agents.

That’s why OWASP’s agentic guidance emphasizes two design principles that keep showing up in real-world failures:

• Least agency: give the agent the minimum autonomy and minimum tool power required to complete the job.
• Strong observability: you must be able to see what the agent decided, what it called, what data it touched, and what it changed.

OWASP’s GenAI Security Project has been publishing agentic guidance (including an “AI Agent Security Cheat Sheet” and “Securing Agentic Applications Guide 1.0”), and the 2026 Agentic Top 10 turns that guidance into an actionable baseline: https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/.

The OWASP Agentic AI Top 10 (2026), explained in plain English

OWASP names these risks with the ASI prefix (Agentic Security Issues). Think of them as the “default threat model” for any system where an AI can plan and execute multi-step work.

ASI01 — Agent Goal Hijack

What it looks like: An attacker (or untrusted content) changes what the agent is “trying to do.” This is broader than classic prompt injection: the agent’s objective gets reframed, subtly or directly, until it takes harmful actions.

Common pattern: The agent is asked to “help” with a task, but hidden or adversarial instructions in the context push it toward a different goal (e.g., exfiltrate data, approve a refund, change a configuration).

Baseline controls:

• Define goals as structured policies (not only natural language).
• Separate “planning” from “execution”: the agent proposes; a policy layer approves.
• Constrain what success means (limits, approvals, allowed targets).

ASI02 — Tool Misuse & Exploitation

What it looks like: The agent has a powerful tool (browser, email, database, CI, ticketing system, payment API). Attackers steer the agent into using that tool in a harmful way.

Common pattern: Tool calls become the exploit path: “Send this email to finance,” “Run this command,” “Update this record,” “Download this file,” “Click through this admin flow.”

Baseline controls (from OWASP’s AI Agent Security guidance):

• Build tools as narrow, allowlisted functions, not a general shell.
• Enforce per-tool scopes (read vs write; specific tables; specific endpoints).
• Require explicit authorization for high-impact actions (payments, user changes, deletes).

ASI03 — Identity & Privilege Abuse

What it looks like: Agents often run with “whatever credentials make the demo work.” In production, that becomes a privileged non-human identity that can be abused like a service account—sometimes worse, because it can be socially engineered through text.

Common pattern: Over-scoped API keys, shared tokens, and long-lived credentials stored in logs, memory, or tool outputs. Once exposed, an attacker uses the agent’s identity to move laterally.

Baseline controls:

• Treat agents as first-class non-human identities with lifecycle management.
• Prefer managed identity, short-lived tokens, and JIT access.
• Make privileges task-scoped and environment-scoped (dev ≠ prod).

ASI04 — Agentic Supply Chain Vulnerabilities

What it looks like: Agentic apps depend on models, plugins, “tools,” MCP servers, RAG pipelines, and third-party APIs. Compromise anywhere in that chain can change what the agent sees or does.

Common pattern: A tool provider is compromised, a dependency update changes behavior, or an “integration” returns adversarial content that the agent treats as instructions.

Baseline controls:

• Inventory your AI supply chain (models, tools, datasets, plugins, servers).
• Pin versions and verify integrity where possible.
• Sandbox and review third-party tools like you would a production dependency.

ASI05 — Unexpected Code Execution (RCE)

What it looks like: Agents that can write/run code, execute scripts, or interact with CI/CD can be coerced into running attacker-controlled code—even if the attacker’s “input” is just text.

Common pattern: “Fix this bug by running these commands,” “Install this package,” “Execute this snippet,” “Open this file and run it.” In agentic workflows, that’s remote code execution with extra steps.

Baseline controls:

• No raw shell access for agents in production environments.
• If code execution is required, run it in hardened sandboxes with network/file restrictions.
• Use allowlisted build steps and signed artifacts.

ASI06 — Memory & Context Poisoning

What it looks like: Agentic apps often store long-term memory (notes, preferences, “facts”) or retrieve context from RAG. If an attacker can poison that memory, they can influence future actions persistently.

Common pattern: A malicious “note,” ticket, doc, or message gets stored as “trusted memory,” and later it rewrites decisions: new approvals, new tool targets, changed recipients, altered constraints.

Baseline controls:

• Separate trusted memory from untrusted context; label and filter both.
• Add validation rules before writing to memory (schemas, allowlists, provenance).
• Make memory write operations privileged and auditable.

ASI07 — Insecure Inter-Agent Communication

What it looks like: Multi-agent systems pass messages and tasks between agents. If those messages aren’t authenticated and integrity-protected, an attacker can forge, replay, or tamper with agent-to-agent instructions.

Common pattern: A “planner” agent delegates to an “executor” agent, but the channel is just JSON over HTTP with weak auth. Now “be helpful” becomes “do this dangerous thing.”

Baseline controls:

• Strong authentication between agents (mutual auth, signed messages).
• Explicit schemas for agent messages; reject out-of-spec payloads.
• Trace IDs and end-to-end audit across agents.

ASI08 — Cascading Failures

What it looks like: Agents often chain actions across tools and other agents. One failure (bad memory, poisoned context, mis-scoped tool) can propagate into systemic failure.

Common pattern: One compromised workflow writes bad state into a shared system (memory store, knowledge base, CRM), and many agents then consume it and amplify it.

Baseline controls:

• Rate limits, circuit breakers, and “safe mode” fallbacks.
• Compartmentalize memory and tool permissions per agent role.
• Automated rollback / remediation playbooks for agent-initiated changes.

ASI09 — Human–Agent Trust Exploitation

What it looks like: People trust fluent output. Attackers exploit that trust by getting the agent to produce authoritative-sounding but malicious instructions or approvals.

Common pattern: “Approve this vendor,” “Wire these funds,” “Disable this control,” “This is from legal.” The agent becomes a social-engineering amplifier inside your org.

Baseline controls:

• UI/UX that shows why the agent decided something (sources, tool traces).
• Mandatory human confirmation for irreversible actions.
• Training: “agent output is a recommendation, not authority.”

ASI10 — Rogue Agents

What it looks like: An agent that acts outside intended constraints: compromised, misconfigured, or optimized toward the wrong objective. “Rogue” can mean malicious, or just dangerously misaligned with policy.

Common pattern: A forgotten background agent keeps running with old permissions; a new tool is added without review; the agent begins acting on stale or poisoned goals.

Baseline controls:

• Strict policy gates for tool additions and permission changes.
• Continuous monitoring of agent behavior drift.
• Kill switches and revocation paths that work in minutes, not days.

The security baseline (what to implement first)

If you’re building or buying autonomous agents, the biggest mistake is treating the “AI layer” as a feature. Treat it as an execution layer—then secure it like one.

Here’s a pragmatic baseline you can implement without boiling the ocean:

1) Adopt “least agency” as an architecture requirement

Least privilege is not enough—agents need least agency:

• Don’t give an agent a general browser if it only needs one internal endpoint.
• Don’t give it “write” if “read + suggest” is sufficient.
• Don’t let it auto-execute multi-step plans without policy evaluation.

This approach is reinforced throughout OWASP’s agent guidance, including the OWASP AI Agent Security Cheat Sheet: https://cheatsheetseries.owasp.org/cheatsheets/AI_Agent_Security_Cheat_Sheet.html.

2) Treat agents as non-human identities (NHI)

From OWASP’s “Securing Agentic Applications Guide 1.0,” the message is consistent: treat non-human identities like humans—provisioning, rotation, and deprovisioning with strong RBAC and short-lived credentials: https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/.

Minimum baseline:

• Separate identities per agent role (planner vs executor vs retriever)
• Short-lived tokens with JIT access
• Explicit “break-glass” approvals for privileged tool calls

3) Build tools as allowlisted functions with scoped permissions

OWASP’s AI Agent Security guidance recommends tool allowlisting and per-tool scoping to reduce tool abuse.

Minimum baseline:

• Every tool has a schema, an allowlist, and explicit operations
• Block sensitive file patterns and secrets by default (env, keys, tokens)
• Add “approval middleware” for high-impact actions

4) Defend against indirect prompt injection (the agent’s new XSS)

Indirect prompt injection happens when hidden instructions are embedded in content the agent fetches (web pages, docs, tickets, code comments). OWASP’s Prompt Injection Prevention guidance is the right starting point: https://cheatsheetseries.owasp.org/cheatsheets/LLM_Prompt_Injection_Prevention_Cheat_Sheet.html.

Minimum baseline:

• Treat all retrieved content as untrusted data (never as policy)
• Strip/neutralize instructions from tool outputs before feeding back to the model
• Separate “data context” from “system instructions” with strict boundaries

5) Instrument everything (strong observability)

You can’t secure what you can’t see.

Minimum baseline:

• Log prompts, tool calls, tool outputs (with sensitive redaction), and decisions
• Attach trace IDs across multi-agent workflows
• Alert on policy violations (unexpected tools, unusual destinations, large data egress)

6) Plan for failures and containment (circuit breakers)

Agentic systems fail differently: they can keep going.

Minimum baseline:

• Rate limits per agent and per tool
• Automated “stop the world” kill switch
• Human review queues when risk scores exceed thresholds

What this means for budgets and risk (the business case)

Security leaders don’t need convincing that autonomous systems increase complexity. What they need is a baseline that keeps risk proportional to value.

• Breach costs are still real. IBM’s Cost of a Data Breach Report 2025 cites a global average breach cost of about $4.4M: https://www.ibm.com/reports/data-breach.
• Agents increase the blast radius. The jump from “generate text” to “execute actions” turns many AI failures into operational incidents: unauthorized email, data access, or system changes.

The ROI of agentic automation is compelling, but only if you can enforce:

• Predictable boundaries (least agency)
• Accountability (non-human identity governance)
• Auditability (strong observability)

A simple way to operationalize the OWASP baseline

If you’re starting in 2026, don’t try to “secure all agents.” Secure your first agentic workflow end-to-end and template it.

Here’s a starter approach we often recommend:

1. Choose one workflow with clear value and bounded scope (e.g., “invoice triage,” “support ticket routing,” “RFP summarization”).
2. Build a policy layer that controls tool access, approvals, and memory writes.
3. Implement a single audit trail that ties prompt → plan → tool calls → outcomes.
4. Red-team the workflow for indirect prompt injection and tool abuse.
5. Only then expand scope and autonomy.

If you need a partner to design the baseline and build secure production-grade agents, that typically spans AI Proof of Concept (PoC) and AI MVP, Cybersecurity, and Cloud Strategy—because agent security is equal parts product, identity, and operations.

The bottom line

OWASP’s Agentic AI Top 10 (2026) is the new “minimum bar” for autonomous systems. If your AI can take actions, the right question isn’t “Is the model accurate?” It’s:

• What can it do?
• What can it touch?
• What happens when it’s wrong—or manipulated?

Build for least agency, treat agents like non-human identities, constrain tools, and instrument everything. That’s what turns agentic AI from a risky demo into a scalable, defensible system.

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Want to build an agentic system that’s secure by design? Codexty helps teams ship production-grade agents with proper guardrails, observability, and identity controls through Web Application Development and Cybersecurity.