Your AI agent can touch customer data, call business tools, and retrieve documents. That makes it the most valuable account in your system, and the one attackers are learning to exploit.
Here's the number that should stop you: AI agents move roughly 16 times more data than human users. A single compromised agent isn't a single-user incident. It's the effective authority of every permission that agent holds, across every system it can reach, Salesforce, M365, Workday, your support queue, your internal docs.
This isn't theoretical. In 2025, a single crafted email in Microsoft 365 Copilot triggered automatic data exfiltration with no clicks from the user. Researchers found 30+ CVEs across major AI coding platforms in December 2025 alone, including a critical flaw that silently exfiltrated secrets and source code.
If you're shipping a customer-facing agent, these are the risks to understand before it goes live.
The OWASP Top 10 for Agentic Applications (2026) maps the failure modes that show up when agents move from pilots into production. Here's each one, translated into what it means for your agent.
| Risk | What it means for your agent |
|---|---|
| ASI01 — Agent Goal Hijack | An attacker redirects the agent's goal via prompt injection or poisoned content. The agent still looks on-task, but serves the attacker. |
| ASI02 — Tool Misuse | The agent uses legitimate tools for unintended actions, unexpected tool chaining, unusual API usage. |
| ASI03 — Identity & Privilege Abuse | The agent escalates through delegated identities, acting beyond normal user capabilities. |
| ASI04 — Supply Chain Vulnerabilities | A compromised tool or plugin turns a clean-looking agent into a malicious one. |
| ASI05 — Remote Code Execution | The agent executes unintended code or unexpected external calls. |
| ASI06 — Memory Poisoning | Attackers seed the agent's memory, RAG index, or summaries with malicious entries. Behavior drifts gradually, not in one obvious step. |
| ASI07 — Insecure Inter-Agent Comms | Message spoofing or tampering between agents. |
| ASI08 — Cascading Failures | One poisoned tool or policy ripples through a network of agents, amplifying into outages or data loss. |
| ASI09 — Human-Agent Trust Exploitation | Social engineering via agents, legit-looking flows that pressure users into harmful approvals. |
| ASI10 — Rogue Agents | Fully misaligned autonomous behavior, agents acting like insiders with broad access. |
The risk that keeps security teams up at night is indirect prompt injection, and it's the one most enterprises underestimate.
Here's how it works. Your agent retrieves a document to answer a question. That document contains a planted instruction:
The agent treats the retrieved content as trustworthy data. It follows the instruction. Before anyone notices, the token and email are gone, exfiltrated through a tool the agent was legitimately authorized to use.
Prompt injection is the AI equivalent of SQL injection, but it operates at the semantic layer, not the network or application layer. That's why traditional perimeter defenses fail against it.
A web application firewall can't see a malicious instruction hidden in a PDF your agent retrieves. Input sanitization can't catch a prompt that looks like a legitimate question. The attack vector is the model's instruction-following behavior itself.
This is why reviewing static configuration isn't enough. Most enterprises are "chasing ghosts," reviewing what they assume the agent does rather than observing what it actually does at runtime. Without runtime truth, you're guessing.
A proper review of a customer-facing agent follows four steps:
The goal isn't a vague security report. It's a ranked findings matrix with proof-of-concept attacks, specific code or config changes, and a retest plan to verify the fixes hold.
Before you commission a full review, you can get a quick read on your own exposure. Paste your system prompt into a free prompt-injection tester and see what breaks. It's a fast way to find out whether your agent is one crafted input away from a data leak.
An attack technique where adversarial users, or third-party data fed to the model, manipulate AI inputs to override system instructions, expose data, or trigger unintended actions.
Standard audits complete within 5 business days from kickoff to report delivery. Larger multi-model or agent-heavy deployments can take 1-2 weeks.
No. Most audits run against a test environment, a read-only API key, or a sandbox. Your production stays untouched.
Related: Prompt Injection: LLM's #1 Risk (2026) · 851 Agent CVEs in 30 Days · AI Security Audit