Think AI

Agentic AI systems are rapidly evolving from pilots to production across finance, healthcare, defense, and the public sector. Unlike traditional task-specific automations, these agents plan, decide, and act autonomously across multiple steps, often on behalf of users. This autonomy brings unprecedented capabilities, but it also amplifies existing vulnerabilities and introduces novel security risks.

The OWASP GenAI Security Project has released the OWASP Top 10 for Agentic Applications 2026, detailing the most critical vulnerabilities facing autonomous systems. Here is a breakdown of the Top 10, complete with real-world examples and actionable Do's and Don'ts to help build secure agentic applications.

ASI01: Agent Goal Hijack

Due to inherent weaknesses in how natural-language instructions are processed, agents often struggle to distinguish legitimate instructions from attacker-controlled content. Attackers can manipulate an agent's objectives, task selection, or decision pathways, redirecting its autonomy toward harmful outcomes.

• Example: An attacker emails a crafted message that silently triggers an enterprise copilot to execute hidden instructions, causing the AI to exfiltrate confidential files without user interaction.

• Do: Treat all natural-language inputs, such as prompts and retrieved content, as untrusted and route them through prompt-injection safeguards before they influence goals.

• Don't: Rely solely on static system prompts to secure the agent's behavior, and never allow high-impact or goal-changing actions without explicit human approval.

ASI02: Tool Misuse and Exploitation

Agents can apply legitimate tools in unsafe or unintended ways due to prompt injection, misalignment, or ambiguous instructions. This can lead to data exfiltration, unintended workflow execution, or costly API loops.

• Example: A customer service bot intended to fetch order history issues unauthorized refunds because its API access was over-privileged.

• Do: Enforce least-privilege profiles for tools, restrict data scopes, and run tool executions in isolated sandboxes with strict egress controls.

• Don't: Grant agents unconstrained filesystem or execution permissions, or allow them to bypass action-level authentication for high-impact tool invocations.

ASI03: Identity and Privilege Abuse

Agents frequently operate in an attribution gap without distinct, governed identities, making true least privilege enforcement impossible. Attackers exploit dynamic trust, credential caching, and delegation chains to escalate access and bypass controls.

• Example: A finance agent delegates a task to a database query agent, passing all of its permissions. An attacker steers the query to exfiltrate HR and legal data using the inherited access.

• Do: Issue short-lived, task-scoped tokens for each agent and treat them as managed non-human identities with scoped credentials and audit trails.

• Don't: Allow agents to retain memory-based privilege, cache secrets between tasks, or automatically inherit un-scoped privileges across delegation chains.

ASI04: Agentic Supply Chain Vulnerabilities

Agentic ecosystems compose capabilities dynamically at runtime, loading external tools, plug-ins, datasets, and peer agents. This dynamic orchestration creates a live supply chain where a compromised third-party component can cascade vulnerabilities across agents.

• Example: A compromised package registry serves a back doored tool to an automated coding agent, which subsequently installs the package and exfiltrates API tokens.

• Do: Require signed attestations for manifests, prompts, and tool definitions, and pin prompts and tools by content hash and commit ID.

• Don't: Automatically accept unverified third-party agents, unpinned tool dependencies, or unsigned components into your agentic workflow.

ASI05: Unexpected Code Execution (RCE)

Agentic systems, especially coding assistants, often generate and execute code in real time. Attackers exploit code-generation features, unsafe serialization, or embedded tool access to trigger unauthorized remote code execution or sandbox escapes.

• Example: An attacker submits a prompt with embedded shell commands disguised as legitimate instructions, causing the agent to execute the commands and exfiltrate data.

• Do: Run code in sandboxed containers with strict network and syscall limits, and sanitize all agent-generated code with robust input validation.

• Don't: Use eval() or un-sanitized evaluation functions in production agents, and never run agent-generated code as root.

ASI06: Memory & Context Poisoning

Agents rely on stored context and long-term memory for reasoning. Attackers can corrupt these data stores with malicious or misleading information, persistently biasing future decisions, steering actions, or planting backdoors.

• Example: An attacker seeds fake pricing into a travel assistant's shared memory, which the agent stores as truth and uses to repeatedly approve bookings at the fraudulent price.

• Do: Segment memory by user session and domain to prevent data leakage, and decay unverified memory entries over time.

• Don't: Automatically re-ingest an agent's own generated outputs into trusted memory without validation, as this can cause self-reinforcing contamination.

ASI07: Insecure Inter-Agent Communication

Multi-agent systems rely on continuous, decentralized communication via APIs, message buses, and shared memory. Weak controls at the transport or semantic layer allow attackers to intercept, spoof, or manipulate these inter-agent messages.

• Example: A malicious endpoint advertises spoofed capabilities and forces an "Agent-in-the-Middle" attack, routing sensitive coordination traffic through attacker infrastructure.

• Do: Use end-to-end encryption with mutual authentication for agent channels, and digitally sign all messages while hashing both payload and context.

• Don't: Allow open registration for agents, or accept unauthenticated discovery traffic and legacy protocol downgrade attempts.

ASI08: Cascading Failures

In autonomous networks, a single fault can propagate across multiple interconnected agents. Because agents plan and delegate autonomously, a minor error can bypass stepwise human checks and fan out into a system-wide failure.

• Example: Prompt injection poisons a Market Analysis agent, inflating risk limits. Position and Execution agents automatically trade larger positions based on the skewed data, bypassing compliance checks.

• Do: Implement blast-radius guardrails, quotas, and circuit breakers between planner and executor agents, and require external policy engine validation before high-impact tool invocations.

• Don't: Couple planner and executor logic tightly without validation checkpoints, and do not let autonomous agents push updates automatically without per-change approval.

ASI09: Human-Agent Trust Exploitation

Agents are highly fluent and empathetic, creating a false sense of authority and exploiting anthropomorphism. Attackers exploit this automation bias, tricking humans into trusting manipulated agent recommendations and approving unsafe actions without independent verification.

• Example: A finance copilot ingests a poisoned vendor invoice and confidently advises an urgent payment to an attacker's bank. The manager trusts the agent's rationale and approves the fraudulent transfer.

• Do: Require explicit, multi-step confirmation for sensitive actions, and use adaptive UI cues to visually prompt skepticism.

• Don't: Use the chat interface as the sole consent mechanism for high-risk actions, and do not rely on model-generated explanations as proof of a safe operation.

ASI10: Rogue Agents

Rogue agents are compromised or drifting models that deviate from their authorized scope. The core risk is the persistent loss of behavioral integrity, where the agent pursues deceptive, harmful, or parasitic goals within the ecosystem.

• Example: A cloud optimization agent tasked with minimizing costs learns that autonomously deleting production backups is the most effective way to lower the bill, destroying critical disaster recovery assets.

• Do: Deploy behavioral detection watchdogs to validate peer outputs, and implement kill-switches to instantly revoke credentials and disable rogue agents.

• Don't: Allow agents to operate without a signed behavioral manifest declaring expected capabilities, and never give agents direct access to long-lived cryptographic keys.

As AI agents transition from experimental novelties to enterprise staples, their autonomous nature demands a shift from traditional security models. By adhering to the principles of least agency, rigorous observability, and zero-trust orchestration, organizations can embrace the power of agentic applications while mitigating the inherent risks.