Source URL: https://embracethered.com/blog/posts/2025/amp-code-fixed-data-exfiltration-via-images/
Source: Embrace The Red
Title: Data Exfiltration via Image Rendering Fixed in Amp Code
Feedly Summary: In this post we discuss a vulnerability that was present in Amp Code from Sourcegraph by which an attacker could exploit markdown driven image rendering to exfiltrate sensitive information.
This vulnerability is common in AI applications and agents, and it’s actually similar to one we discussed last year in GitHub Copilot which Microsoft fixed.
Exploit Demonstration For the proof-of-concept I use a pre-existing demo that created a longer time ago.
AI Summary and Description: Yes
Summary: The text highlights a vulnerability in Amp Code from Sourcegraph, showcasing how markdown-driven image rendering could allow attackers to exfiltrate sensitive information. This issue is notable as it echoes a similar vulnerability previously identified in GitHub Copilot, emphasizing ongoing security concerns in AI applications.
Detailed Description: The text elaborates on a security vulnerability present in Amp Code from Sourcegraph that exposes sensitive information through markdown-driven image rendering. This type of vulnerability is particularly relevant in the context of AI applications, as it underscores potential risks and exploits that can affect software developed for AI purposes.
– **Vulnerability Description**: The vulnerability allows an attacker to exploit a feature that renders images from markdown. By manipulating how images are rendered, an attacker can potentially steal sensitive data.
– **Relation to AI Applications**: The context of the vulnerability being common in AI applications signifies a broader trend where AI-driven software may inherit similar weaknesses, heightening the need for security vigilance in this area.
– **Historical Reference**: The mention of a past vulnerability in GitHub Copilot indicates that this is not an isolated incident but part of a larger pattern where AI tools may be susceptible to cybersecurity threats. This historical context could help security professionals understand how to anticipate and mitigate these types of risks in future AI projects.
– **Proof-of-Concept Mention**: The reference to a proof-of-concept for demonstrating this exploit underscores the practical implications of this vulnerability, serving as a call to action for developers to review their implementations to prevent exploitation.
Overall, the insights from this post underline the imperative for organizations developing or utilizing AI tools to prioritize robust security practices, keep abreast of vulnerabilities, and adopt best practices for securing software against similar risks.