Description

Improper neutralization of input used for llm prompting in Microsoft Edge for iOS allows an unauthorized attacker to perform spoofing over a network.

Executive Summary

Technical Details

Mitigations

References

Additional Metadata

{
  "score": "5.4",
  "pubDate": "2026-08-28T20:19:41.820Z",
  "pubdate": "2026-08-28T20:19:41.820Z",
  "executiveSummary": "A vulnerability classified as Improper Neutralization of Input During Prompt Generation exists within the Microsoft Edge browser for iOS.\nThis flaw permits an unauthorized remote attacker to conduct spoofing attacks over a network by manipulating the context processed by the Large Language Model (LLM) integration.\nThe vulnerability fundamentally stems from the failure of the application to adequately sanitize or delimit user-supplied input before it is consumed by the LLM, allowing for prompt injection scenarios.\nThe potential impact includes the unauthorized manipulation of LLM-generated outputs, which can be leveraged to deceive users, misrepresent information, or facilitate phishing campaigns by appearing to originate from trusted browser-integrated AI services.\nAttackers do not require local access to the device, as the exploitation can be triggered over a network, effectively lowering the barrier for entry.\nThis represents a significant security risk to the integrity of AI-assisted browsing features, potentially undermining user trust in the browser's security boundaries and data processing capabilities.",
  "technicalDetails": "The vulnerability resides in the input handling pipeline responsible for passing contextual data to the LLM backend within Microsoft Edge for iOS. The root cause is a deficiency in input sanitization, where user-supplied content is improperly neutralized prior to being injected into the prompt assembly process.\nIn a standard execution flow, the application aggregates browsing context and user input to form a comprehensive prompt for the integrated LLM. Because the application fails to distinguish between trusted control instructions and untrusted user-provided data, an attacker can supply specially crafted inputs that break out of the intended prompt structure.\nThe exploitation process follows a specific attack flow: 1) An attacker hosts malicious content or intercepts network traffic to inject a payload into a site loaded by the victim within the browser. 2) When the user invokes AI-assisted features (such as page summarization or analysis), the browser inadvertently parses the attacker's payload as a system instruction or a command override for the LLM. 3) The LLM processes these instructions as part of its operational directives, allowing the attacker to influence the model's behavior, style, or the veracity of the provided response.\nThis form of prompt injection allows for the generation of spoofed or malicious information that the user might perceive as a legitimate output from the browser's built-in AI tool. By manipulating the context, the attacker can force the LLM to output content that redirects the user to fraudulent landing pages, suggests malicious files, or presents misinformation under the guise of an AI-verified summary.\nThe attack is network-exploitable, requiring no prior authentication or local privilege escalation. The scope is limited to the LLM-interaction context within Microsoft Edge for iOS, but the impact is significant due to the high trust users place in browser-based AI integrations. Successful exploitation does not require executing binary code on the target device; instead, it exploits the logic of the AI model to perform the spoofing. This semantic exploitation bypasses traditional memory-safe mitigations, as the vulnerability is situated in the logical handling of natural language commands rather than memory corruption.\nPost-exploitation activities involve the generation of misleading content that appears to be endorsed by the browser’s AI, which can be used to facilitate social engineering or credential harvesting while evading typical web-based filters."
}