CVE-2026-70331 in Edge
Summary
by MITRE • 08/28/2026
Improper neutralization of input used for llm prompting in Microsoft Edge for iOS allows an unauthorized attacker to perform spoofing over a network.
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Analysis
by VulDB Data Team • 08/28/2026
The vulnerability described involves improper neutralization of user-supplied input within the context of large language model (LLM) prompting mechanisms integrated into Microsoft Edge for iOS. This specific flaw represents a class of security issues often categorized under CWE-74, which refers to Improper Neutralization of Special Elements used in an XML or Command Injection, though in this modern context it extends to prompt injection and semantic manipulation rather than traditional code execution. The core technical deficiency lies in the application's failure to adequately sanitize or validate input data before it is processed by the LLM component. When a user interacts with features that utilize generative AI capabilities, such as Copilot or smart search suggestions, the system accepts external inputs without sufficient filtering for malicious patterns designed to override default behavioral constraints.
This lack of rigorous input validation allows an unauthorized attacker to perform spoofing over a network through what is known as prompt injection. By crafting specific sequences of text that exploit ambiguities in how the LLM interprets instructions versus data, an attacker can trick the model into ignoring its safety guidelines or system prompts. This could result in the leakage of sensitive context, such as previous conversation history or internal configuration details, if those are inadvertently included in the prompt window. Furthermore, it enables the execution of unintended actions by manipulating the AI to generate responses that appear legitimate but serve malicious purposes, effectively spoofing the user's trust in the application's output.
From an operational impact perspective, this vulnerability undermines the integrity and confidentiality assurances provided by the browser environment on iOS devices. Users may be deceived into believing they are interacting with a neutral, helpful assistant when they are actually engaging with manipulated logic that could lead to social engineering attacks or data exfiltration. The network-based nature of the attack vector means that remote exploitation is possible without requiring local access or physical proximity to the device. This aligns with MITRE ATT&CK techniques related to Command and Scripting Interpreter, specifically those involving prompt injection where attackers leverage natural language processing systems to bypass security controls.
Mitigation strategies must focus on implementing robust input sanitization protocols at both the client-side application layer and within the LLM inference pipeline. Developers should employ strict allow-lists for acceptable inputs rather than relying solely on deny-lists, which are often incomplete against novel attack vectors. Additionally, integrating defense-in-depth measures such as output filtering to detect and block potentially harmful responses generated by the model is critical. Security teams should also consider implementing rate limiting and anomaly detection systems that monitor for unusual patterns in user prompts indicative of injection attempts. Regular security audits focusing on AI-specific vulnerabilities are essential to ensure that evolving LLM architectures remain resilient against these sophisticated forms of input manipulation.