Generative AI (GenAI) can be exploited to escalate access through methods like prompt crafting, plugin misuse, or weak controls. This article explores these vulnerabilities and outlines how to mitigate them, preventing unauthorized elevation within your enterprise systems.

The New Frontier of Privilege Escalation

Privilege escalation is a foundational concept in cybersecurity, describing the method a threat actor uses to gain elevated access beyond their authorized level. Traditionally, this involved exploiting system vulnerabilities or misconfigurations to move from a standard user account to an administrator account. However, the rapid integration of GenAI into enterprise workflows has created a new, more abstract attack surface where the target is not just code, but logic itself.

The core challenge is that GenAI’s greatest strength, its ability to comprehend and execute complex, natural language instructions, is also its primary weakness. A threat actor no longer needs to find a bug in the software; they can simply trick the Large Language Model (LLM) into misusing its existing, legitimate permissions. This is the new frontier of AI privilege escalation: a malicious actor co-opts the AI, turning it into an unwilling accomplice to bypass security controls. This strategic shift moves the battleground from predictable code execution to the fluid and unpredictable domain of contextual manipulation. Why prioritize browser security in 2025? Because the browser is the primary conduit for these new AI interactions, making it the most critical point for observation and control.

Key Vectors for AI Elevation

The pathways to achieve AI elevation are varied, often exploiting the seams between the user, the AI model, and the data it can access. These attacks are not always brute-force; they are frequently subtle, exploiting overly permissive integrations and user trust.

Deceiving the Machine with Prompt Injection

Prompt injection has become a critical vulnerability in the GenAI ecosystem. It involves crafting malicious instructions that override the LLM’s original purpose, effectively tricking it into performing unauthorized actions.

  •       Direct Injection: In this scenario, a malicious user intentionally crafts a prompt to bypass the AI’s built-in safety features. For example, they might instruct the model to ignore its previous directions and instead reveal sensitive system information or execute a restricted function. This is akin to social engineering the AI itself.
  •       Indirect Injection: This method is more insidious. A threat actor hides a malicious prompt within an external data source that the LLM is expected to process, such as a webpage, a document, or an email. When the AI ingests this poisoned data, the hidden command executes, potentially leading to data exfiltration or AI elevation without the user’s knowledge. Imagine an AI-powered email assistant that summarizes incoming messages; a malicious email could contain a hidden instruction telling the assistant to forward all of the user’s other emails to an external address.

Exploiting Insecure AI Plugins and Browser Extensions

The functionality of GenAI is often extended through third-party plugins and browser extensions, but these add-ons introduce significant risk. Many extensions are designed with broad permissions to access clipboard data, read page content, or intercept user inputs to function correctly. While seemingly innocuous, these elevated permissions can be a gateway for malicious activity. If a threat actor compromises an extension or publishes a malicious one, they can achieve privilege escalation (TA004) within the browser context.

These insecure extensions can lead to:

  •       Session Hijacking: Malicious plugins can steal authentication cookies and session tokens, giving attackers direct access to a user’s SaaS applications and internal systems.
  •       Silent Data Exfiltration: Some extensions are designed to log user prompts or interactions and secretly send that data to a third-party server, creating a continuous data leak that is invisible to the user.
  •       Inter-Plugin Data Leakage: When multiple extensions with overlapping permissions run in the same browser, sensitive data can flow between them unintentionally. One plugin could act as a bridge to siphon data being processed by another, even if neither is explicitly malicious. It’s crucial to manage extension “premissions” and configurations carefully, as even minor oversights can be exploited.

Weaknesses in GenAI Access Control

The rush to deploy AI has often led to inadequate GenAI access control. When AI models are granted excessive, overpowered access to data, they become high-value targets. If a model with broad access to sensitive information is compromised, the blast radius is immense. This is compounded by the problem of “Shadow AI,” where employees use unvetted, public AI tools without organizational approval, operating completely outside of any security governance.

Effective GenAI access control requires a move away from static whitelists toward dynamic, context-aware authorization. The principle of least privilege must be strictly enforced, granting AI systems only the minimal permissions needed for their specific tasks. Without granular, risk-based permissions, an organization might, for example, give a marketing intern access to the same AI-powered legal analysis tool as the general counsel, creating a significant internal risk of data exposure.

Insecure Integrations and API Misuse

As organizations integrate GenAI into their internal applications via APIs, another risk vector emerges. A misconfigured API can act as an open gateway for a threat actor. If authentication and authorization controls are not implemented correctly, attackers can exploit these weaknesses to gain unauthorized access to the underlying model and, more critically, the data being processed through it. These vulnerabilities allow for the systematic exfiltration of data at scale, often going undetected for extended periods.

Anatomy of a GenAI Privilege Escalation Attack

To understand the real-world impact, imagine a scenario. A threat actor develops a seemingly helpful Chrome extension, let’s call it “Code-Helper”, that promises to format code snippets copied from the web. The extension requests broad permissions, including access to clipboard data and the ability to read all website data, which unsuspecting developers grant to get the promised functionality.

  1. Initial Infiltration: A developer at a financial tech company installs Code-Helper to improve their workflow. Once installed, the extension begins silently monitoring the user’s clipboard and browsing activity.
  2. Reconnaissance and Credential Access: The developer copies a snippet of code from a company wiki page that includes a temporary access key for a staging database. The malicious extension captures this key from the clipboard. Simultaneously, the extension harvests authentication cookies from the browser’s storage, giving the attacker access to the developer’s active sessions in various SaaS applications.
  3. Indirect Prompt Injection: The developer then uses a sanctioned, internal GenAI chatbot to help debug a complex function. They paste a large block of code into the chatbot. The malicious extension intercepts this action and uses indirect prompt injection, subtly appending a hidden instruction to the code before it’s sent to the LLM. The instruction tells the LLM to use its integrated functions to query the staging database with the previously stolen key and exfiltrate the schema.
  4. AI Elevation and Lateral Movement: The LLM, following its instructions, executes the malicious command. It connects to the database, extracts the schema, and encodes the information within a seemingly benign response to the developer’s original query. The threat actor’s server receives the schema. Now understanding the database structure, the attacker uses the stolen SaaS session cookies to pivot to other internal systems, escalating their privileges from a single developer’s account to a wider-reaching system compromise.

Mitigation: A Browser-Centric Security Strategy

Traditional perimeter-based tools like CASBs and Secure Web Gateways were not designed to inspect the real-time, in-browser interactions that define modern AI usage. They cannot reliably distinguish between personal and corporate accounts, see the content of user prompts, or prevent data from being pasted into a chat window. To close these critical security gaps, organizations must adopt a browser-centric approach that provides deep visibility and granular control.

Map and Monitor All GenAI Usage

The first step is to gain complete visibility. Organizations must map how GenAI is being used across the company, monitoring which tools are in use, who is using them, and what kind of data is being shared. This allows security teams to build a clear usage profile, identify high-risk “Shadow AI” applications, and create a governance plan that enables productivity while ensuring data protection. LayerX provides a full audit of all SaaS applications, allowing organizations to map GenAI usage and identify unsanctioned tools.

Implement Granular GenAI Access Control

Apply role-based access controls to limit AI tool access based on job function and data sensitivity. Block the use of personal GenAI accounts for corporate work and mandate access through enterprise accounts that offer stronger security and privacy safeguards. LayerX helps enforce this governance by applying granular, risk-based guardrails over all SaaS usage, ensuring that only authorized users can access specific tools and data.

Govern and Control Browser Extensions

Organizations need tools to discover, vet, and control the browser extensions used in their environment. This involves blocking extensions that exhibit risky behaviors or request excessive permissions, regardless of their stated function. A secure enterprise browser extension can monitor the behavior of other extensions in real time, neutralizing threats like session hijacking or data exfiltration before they can cause harm.

Inspect In-Browser Behavior to Prevent Prompt Injection

Security solutions must be able to monitor DOM-level interactions to detect and block prompt injection and unauthorized data access in real time. By analyzing the content and context of data being entered into prompts, a browser-centric solution can identify and neutralize malicious instructions before they reach the LLM. LayerX’s solution tracks all file-sharing activities and controls user actions within SaaS apps to prevent data leakage.

Enforce GenAI Data Loss Prevention (DLP)

A critical line of defense is implementing robust GenAI DLP policies that control what data can be pasted, uploaded, or typed into AI prompts. This prevents both accidental and malicious exfiltration of sensitive information like source code, customer PII, or internal financial data. LayerX allows organizations to restrict the sharing of sensitive information with LLMs, mitigating the risk of data leakage through GenAI tools.

By shifting focus to the browser, where the interaction between users, data, and AI actually occurs, enterprises can effectively manage the risks of AI privilege escalation. This approach allows security teams to harness the transformative power of GenAI without compromising their security posture.