A common mistake is assuming the user must click, approve, or intentionally trigger harm. Zero-click attacks can abuse the agent’s own context, memory, or tool access without user interaction. Security teams should assess trusted inputs, tool invocation paths, and downstream side effects, then limit exposure with least privilege, validation, and runtime monitoring.
Why This Matters for Security Teams
Zero-click attacks against AI assistants and agents are dangerous because the attacker is not trying to trick a person into approving a bad action. They are trying to manipulate the assistant’s trusted inputs, memory, or tool chain so the system itself performs the harmful step. That shifts the problem from phishing awareness to runtime trust boundaries, and it is why guidance in the OWASP OWASP Top 10 for Agentic Applications 2026 and the NIST AI Risk Management Framework focuses on context, provenance, and operational controls rather than user prompts alone.
Security teams often overestimate the value of the approval step and underestimate how much damage an agent can do with delegated access. If a model can read email, query a ticketing system, call an API, or chain tools across services, a malicious instruction hidden in content may be enough to create data exposure, unauthorized transactions, or privilege escalation. NHIMG’s The State of Non-Human Identity Security shows how weak visibility and over-privilege already create exposure across non-human identities. In practice, many teams discover zero-click paths only after an assistant has already processed the hostile input and triggered the side effect, rather than through deliberate testing.
How It Works in Practice
The core failure is assuming the assistant is a passive parser. In reality, an AI assistant or autonomous agent may ingest untrusted content from email, chat, web pages, documents, tickets, or retrieval systems and then act on it using real credentials. A zero-click attack succeeds when the malicious content is embedded in a trusted channel and the agent’s policy, memory, or tool-selection logic treats it as legitimate context. That is why static IAM alone is not enough: role assignments do not describe what the agent is trying to do at runtime.
Current best practice is to combine least privilege with runtime controls that understand intent, task scope, and data sensitivity. That usually means:
- issuing short-lived credentials per task instead of long-lived static secrets,
- binding tool use to workload identity and request-level attestation,
- evaluating policy at execution time, not only at onboarding or deployment,
- restricting the assistant’s ability to chain tools without step-up checks, and
- logging prompt, retrieval, and action traces so defenders can reconstruct abuse paths.
For workload identity, teams are increasingly using patterns from SPIFFE and token exchange to prove what the agent is before granting access, while policy engines such as Open Policy Agent or Cedar evaluate whether the requested action is acceptable in the current context. NHIMG’s OWASP Agentic Applications Top 10 discusses the same pattern from the agentic risk perspective. This guidance breaks down in highly interconnected environments where agents can invoke many downstream systems through shared service accounts, because the blast radius expands faster than policy teams can map it.
Common Variations and Edge Cases
Tighter runtime controls often increase latency and operational overhead, so organisations have to balance safety against user experience and automation throughput. That tradeoff matters most when an assistant is embedded in customer support, engineering workflows, or enterprise search, where even a small amount of friction can push users to bypass controls.
There is no universal standard yet for how much autonomy an agent should have before requiring step-up confirmation. Current guidance suggests using stronger guardrails when the agent can touch money movement, identity management, external communications, or production systems. For lower-risk summarisation tasks, the control set can be lighter, but only if retrieval is tightly scoped and tool use is blocked by default. Anthropic’s first AI-orchestrated cyber espionage campaign report is a reminder that autonomous workflows can be bent toward real attacker goals very quickly. Teams should also treat hidden instructions in documents, web pages, and retrieved context as a hostile input class, not just a content moderation problem. That distinction becomes especially important when agents retain memory across sessions or operate inside multi-agent pipelines with shared state.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A3 | Zero-click abuse exploits prompt, context, and tool trust in agent workflows. |
| CSA MAESTRO | TRM-2 | MAESTRO addresses threat modeling for autonomous agents and chained tool use. |
| NIST AI RMF | AI RMF covers governance, measurement, and monitoring for AI system risk. | |
| OWASP Non-Human Identity Top 10 | NHI-01 | Agents rely on secrets and workload identities that attackers can abuse. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access is central when agents can invoke tools and APIs. |
Limit tool authority, sanitize context, and require runtime checks before any agent action.
