Risk type: Prompt Injection
Prompt injection
Prompt injection is when hidden or malicious instructions cause an AI system to ignore its intended rules and do something unsafe or unintended.
Quick answer
The fastest way to reduce AI risk is to control what can be typed, pasted, and uploaded in the browser. Combine governance (approved tools and data boundaries) with browser-layer enforcement. When users browse unknown destinations as part of AI workflows, isolation reduces endpoint exposure by running web content in an isolated container and streaming only rendered output; sessions are deleted after use.
When you need this
- Employees paste internal data into AI prompts to move faster.
- You need policy enforcement in the browser, not just training and documents.
- You want to allow AI productivity while preventing sensitive data loss.
Last updated
2026-01-29
Affected tools
- AI chat tools
- AI copilots
- Browser-based AI agents
- RAG-enabled assistants
How it usually happens in the browser
- A user pastes untrusted content (web pages, emails, tickets) into an AI tool and asks it to summarize or act.
- The untrusted content contains hidden instructions (“ignore previous instructions”, “exfiltrate secrets”).
- An AI agent with tool access (browser, email, docs) follows the injected instructions.
- The model leaks data, performs unsafe actions, or generates outputs that cause users to take risky steps.
- Attackers iterate quickly because prompt injection is cheap and hard to detect perfectly.
What traditional defenses miss
- Classic input validation isn’t designed for natural-language instruction attacks.
- Users assume “summarize this page” is safe, even when the page is adversarial.
- Security tooling often doesn’t track the chain: untrusted web content → prompt → model output → user action.
- Agents with tool access amplify risk because injected instructions can trigger real actions.
Mitigation checklist
- Treat all external content as untrusted input to AI systems; use strong system prompts and guardrails.
- Constrain tools and permissions: least privilege for agents (what they can read/write/click).
- Separate data and instructions: label sources, isolate retrieval results, and sanitize/strip hidden text where possible.
- Add human confirmation for high-risk actions and prevent models from directly executing sensitive operations.
- Monitor and test: run prompt-injection red-teaming against your most used AI workflows.
How isolation helps
- Isolation can reduce risk from untrusted web content by running pages in isolated containers and limiting what reaches the endpoint environment.
- When teams browse unknown sites to “feed” content into AI tools, isolation reduces exposure to drive-by threats and malicious downloads.
- Isolation complements AI guardrails by reducing the overall browser attack surface in prompt-heavy workflows.
FAQs
Is prompt injection just “jailbreaking”?
They’re related, but prompt injection often targets an application or agent workflow (tools, retrieval, actions), not just the model’s content policy.
Can we fully prevent prompt injection?
Not reliably. You can reduce risk with guardrails, least privilege, tool constraints, and human approvals for risky actions.
Why is prompt injection relevant to browsers?
Browsers are where untrusted content lives. People paste web content into AI tools, and browser-based agents can be instructed by that content.
What’s the fastest mitigation?
Limit tool permissions and require human confirmations for high-impact actions, and treat external web content as untrusted input.