TL;DR

AI-native "agentic" browsers can take multi-step actions on a user’s behalf, which changes the risk profile of web browsing. In December 2025, Gartner advised most organizations to block AI browsers for now while enterprise controls catch up. Prompt injection is no longer just about manipulating text. It can influence real actions taken inside authenticated browser sessions. The right path forward is a controlled pilot, clear acceptable-use rules, and upgraded monitoring/incident response before broader adoption.

AI Browsers are Inevitable

AI browser extensions have been available for a while. But the launch of AI-native browsers that include agentic capabilities means that browsers have the ability to take multi-step actions on a user's behalf.

In December 2025, Gartner published an advisory recommending that most organizations block AI browsers for now, at least until controls and governance capabilities catch up. [1]

Historically, prompt injection attacks influenced model outputs (the resulting text). With agentic AI browsers, prompt injection can influence real actions because the agent is operating inside the same browser session and with the effective permissions of the user.

Shadow AI adds pressure. A recent survey reported that employees will use AI tools that violate policy if it helps them finish work faster. [2]

Security Analysis

Risks

AI browsers introduce risks that aren’t fully addressed by the assumptions and controls most enterprises rely on today. Here are the major ones:

• Visibility loss - Many AI browsers do not yet offer the same centralized audit logging, SIEM feeds, data-retention alignment, and enterprise reporting that security teams expect from mature browsers. For example, OpenAI's Atlas is explicitly out of scope for SOC 2/ISO attestations, does not emit compliance API logs, and does not integrate with SIEM or eDiscovery today. Some Atlas-specific data types may also not be covered by Enterprise retention/deletion requirements, and Atlas-specific data is not region-pinned. [3]

• Broken isolation assumptions - Modern browsers enforce security boundaries like same-origin policy and tab isolation. Agentic AI browsers weaken practical isolation assumptions because the agent can read content and perform actions across tabs and contexts.

  Practical example: I asked an AI browser (Atlas) to submit a form in one tab using a summary of content from another tab, and it completed the task.

• Lack of least privilege - Agentic AI browsers typically operate with the user's effective privileges in the browser session (cookies, active logins, and whatever the user can access). That makes high-value users (executives, finance, IT, and executive assistants) especially attractive targets for prompt-injection and social engineering. Gartner's warning highlights risks including unsafe autonomous actions and indirect prompt injection.

• Data commingling - When browser agents can "see" more context (tabs, history, page content, connected services), you increase the odds that personal and professional data, or low-risk and high-risk workflows, get mixed in ways your policies don't cover.

Controls (and their limits)

AI browser vendors are adding controls, but executives should assume those controls are nascent and evolving.

Prompt injection defenses

Defenses are improving and the industry is getting better at labeling prompt injection behavior through public benchmarks and datasets. However, this is also an active adversarial space: defenses will improve, and so will attack techniques.

Human in the loop

Most agentic AI browsers provide opportunities for human oversight.

In Atlas, agent mode displays an initial, first-run warning. It also provides the ability to pause/interrupt/take over, and a "logged out" mode where the agent won’t use existing cookies or accounts unless you explicitly approve. Atlas also documents key boundaries for agent mode (e.g., it can’t access saved passwords or autofill data, and it can’t install extensions). [4]