DifyTap Vulnerabilities CVE-2026-41948 (CVSS 9.4) Wiretap AI

The Dify open-source AI platform has been identified with a cluster of critical vulnerabilities, collectively termed "DifyTap," which expose user chat histories and sensitive data to unauthorized access. These flaws, including CVE-2026-41947, CVE-2026-41948, CVE-2026-41949, and CVE-2026-41950, present significant risks for organizations using Dify for AI application development and management. The most severe of these, CVE-2026-41948, carries a CVSS score of 9.4, indicating critical severity.

These vulnerabilities could allow attackers to establish persistent data exfiltration channels and access confidential documents across tenants without proper authorization by traversing internal APIs. While security researchers from Zafran discovered these flaws, there is currently no public information indicating active exploitation in the wild.

The implications of DifyTap extend to any organization using Dify to power customer-facing chatbots or internal AI applications that handle sensitive user prompts, model responses, or document uploads. Proactive patching and specific mitigation strategies are essential to protect the integrity and confidentiality of AI-driven interactions.

What is DifyTap and why is it critical?

DifyTap refers to a set of four distinct vulnerabilities found in Dify, an open-source platform designed for building and managing AI applications, that collectively allow for the unauthorized access and exfiltration of sensitive AI data. The platform's role as an orchestration layer makes it a central point for user interactions and proprietary information within AI applications, which makes the vulnerabilities critical. Dify has over 10 million pulls of its API image on Docket and tens of thousands of Internet-facing instances, so the potential reach of these vulnerabilities is wide.

The most critical of these flaws is CVE-2026-41948, a Plugin Daemon path traversal vulnerability with a CVSS score of 9.4, which could allow unauthenticated access to internal API components. CVE-2026-41947, a tracing configuration flaw, scores 9.1 CVSS, allowing attackers to "wiretap" AI chat histories. The remaining two, CVE-2026-41949 (unauthorized document preview) and CVE-2026-41950 (cross-file user access), both hold a CVSS score of 6.5. Together, these vulnerabilities threaten data privacy and the security of AI-powered interactions.

Impact

The DifyTap vulnerabilities collectively allow an attacker to gain unauthorized access to sensitive data and internal systems, risking confidentiality and potentially integrity. An attacker using these flaws could intercept and exfiltrate private AI chat histories, user prompts, and model responses from vulnerable Dify-powered applications. This could expose proprietary business logic, personally identifiable information, or other confidential communications depending on the nature of the AI application.

Specifically, the ability to preview documents uploaded by other tenants without authorization (CVE-2026-41949) and to leak file content across users within a tenant by using a document's Universally Unique Identifier (UUID) in a prompt (CVE-2026-41950) directly compromises data stored within the Dify environment. The Plugin Daemon path traversal (CVE-2026-41948) is an architectural flaw that, while currently limited in its immediate data exposure, is a fundamental vulnerability that could be used for more severe exploits if new internal endpoints are introduced or modified. Any organization using Dify as an orchestration layer for AI applications, particularly those handling customer interactions or internal confidential data, is at risk of sensitive data exposure.

Exploitation chain

The DifyTap vulnerabilities use different aspects of the Dify platform, ranging from API configuration to document handling and internal service access. Understanding each exploitation chain is important for developing targeted detection and remediation strategies.

CVE-2026-41947: Tracing Configuration Flaw (CVSS 9.1)

This high-severity vulnerability allows an attacker to hijack the tracing functionality of Dify applications and creates a persistent data exfiltration channel. Tracing in AI applications allows for the monitoring and profiling of their operations. The exploitation process involves several steps:

  1. Account Creation: An attacker first creates their own Dify account.
  2. Application Identification: The attacker identifies a public-facing AI application built on Dify.
  3. App ID Acquisition: The attacker obtains the internal Application ID (App ID) of the target public application.
  4. Tracing API Manipulation: The attacker calls Dify's tracing configuration API and registers their own tracing backend.

By redirecting the tracing data to their controlled backend, the attacker can establish a continuous feed of all messages and responses exchanged within the application. This includes sensitive user prompts, model responses, and full chat histories. Such an exploit directly mirrors our prior analysis of AI chatbot data leaks, showing how critical conversation data can be silently siphoned off.

CVE-2026-41948: Plugin Daemon Path Traversal (CVSS 9.4)

This critical vulnerability targets the Plugin Daemon, an internal service Dify uses to manage and execute plugins. It is an architectural flaw that allows unauthenticated access to exposed internal components.

  1. Unauthorized Access: An attacker sends unauthenticated requests to the Dify instance.
  2. Path Traversal: By exploiting the path traversal vulnerability, the attacker can access parts of the internal Plugin Daemon API that should be restricted.

While the immediate impact observed by researchers was limited to accessing debug and performance data (pprof), this flaw is considered fundamental. Its presence means any future updates or new endpoints within the Plugin Daemon could instantly become high-severity vulnerabilities, providing wider access or control to an attacker. This demonstrates a common risk in AI platforms, as discussed in our insights into AI assistant vulnerabilities like those in Microsoft 365 Copilot, where seemingly minor access flaws can escalate.

CVE-2026-41949: Unauthorized Document Preview (CVSS 6.5)

This vulnerability allows an attacker to view the content of sensitive documents uploaded by other tenants without proper authorization checks.

  1. UUID Discovery: An attacker must first discover the Universally Unique Identifier (UUID) associated with a target document. The method of UUID discovery is not detailed but could involve other information disclosure flaws or brute-force attempts in specific scenarios.
  2. Content Viewing: With the document's UUID, the attacker can access a preview endpoint in Dify to render the document's content, bypassing all permission checks.

CVE-2026-41950: Cross-File User Access (CVSS 6.5)

This vulnerability extends the risk of unauthorized document access by allowing an attacker to coerce an AI application into leaking document content.

  1. UUID Discovery: Similar to CVE-2026-41949, the attacker needs to discover the UUID of a target document.
  2. Content Leakage: The attacker crafts a malicious prompt for an AI application built on Dify, incorporating the discovered document UUID. The AI application, without performing adequate authorization checks, processes the prompt and subsequently leaks the content of the file associated with the UUID as part of its response. This behavior is reminiscent of our investigation into "ChatGPT Agent" flaws that led to Gmail data leaks, where AI agents can be manipulated to expose sensitive information.

Affected products and versions

The DifyTap vulnerabilities impact specific versions of the Dify open-source AI platform. The remediation status varies across the four identified CVEs.

  • CVE-2026-41947 (Tracing Configuration Flaw)
  • CVE-2026-41949 (Unauthorized Document Preview)
  • CVE-2026-41950 (Cross-File User Access)

These three vulnerabilities affect Dify platform versions prior to 1.14.2. They have been addressed in Dify version 1.14.2 and subsequent releases.

  • CVE-2026-41948 (Plugin Daemon Path Traversal)

This critical vulnerability affects Dify platform versions prior to the latest build from the GitHub master branch. Dify version 1.14.2, which patches the other three CVEs, is still vulnerable to CVE-2026-41948. The fix for this vulnerability has been merged into the Dify GitHub repository, requiring users to deploy the most recent version by building from source to fully mitigate the risk.

Detection

Detecting the exploitation or presence of DifyTap vulnerabilities requires a multi-layered approach, focusing on network traffic, application logs, and system configurations within Dify deployments. Given the lack of in-the-wild exploitation reports, detection efforts should prioritize proactive monitoring and anomaly detection.

  • Web Application Firewall (WAF) Logs: Implement and review WAF logs for attempts to access the Plugin Daemon API from unauthenticated or unusual sources, especially for Dify instances running version 1.14.2. Look for patterns indicative of path traversal attempts or requests to debug/pprof endpoints that originate outside of expected internal network segments.
  • Dify Application Logs: Monitor Dify application logs for unusual activity related to tracing configuration changes. Look for log entries indicating new tracing backends being registered, particularly if these registrations are initiated by unknown user accounts or external IP addresses not typically associated with Dify administration.
  • API Access Monitoring: Implement granular logging and monitoring of API calls to Dify's tracing configuration endpoint. Anomalies might include a high volume of tracing configuration requests or modifications from unexpected user accounts or registrations pointing to external, untrusted hosts.
  • Document Access Patterns: Monitor access logs for Dify's document preview endpoints. Look for patterns of sequential UUID lookups or attempts to access documents using UUIDs by unauthenticated users or users who lack proper authorization for the specific tenant or document.
  • AI Application Prompt Analysis: For Dify-powered AI applications, implement logging and analysis of user prompts and model responses. Look for prompts containing UUID-like strings, especially if these prompts lead to the AI application revealing file content that should otherwise be restricted.
  • Network Flow Monitoring: Establish network flow monitoring to identify unexpected outbound connections from Dify instances, particularly those related to newly registered tracing backends.

Remediation

Prompt and full remediation is important for Dify instances to address the DifyTap vulnerabilities and protect sensitive AI chat histories and data. The remediation strategy involves both patching and the application of interim mitigations where immediate patching is not feasible.

  • Patch Dify to Version 1.14.2 or Newer:
  • Upgrade Dify instances to version 1.14.2 or a later release to address CVE-2026-41947 (Tracing Configuration Flaw), CVE-2026-41949 (Unauthorized Document Preview), and CVE-2026-41950 (Cross-File User Access). This patch bundle resolves the immediate risks associated with tracing manipulation and unauthorized document access.
  • Deploy the Latest Dify Version from GitHub (for CVE-2026-41948):
  • For CVE-2026-41948 (Plugin Daemon Path Traversal), the fix has been merged into the Dify GitHub repository. Organizations must build and deploy the most recent version of Dify from the GitHub master branch to fully mitigate this critical vulnerability.
  • Implement Web Application Firewall (WAF) Rules (Interim Mitigation for CVE-2026-41948):
  • For organizations currently operating on Dify version 1.14.2 and unable to immediately deploy the latest build from GitHub, it is recommended to implement specific Web Application Firewall (WAF) rules. These rules should be designed to detect and block unauthorized access attempts to the internal Plugin Daemon API, specifically targeting patterns indicative of path traversal and access to debug/pprof endpoints.
  • Regular Security Assessments:
  • Conduct regular security assessments, including penetration testing and vulnerability scanning, on all Dify deployments and the AI applications built upon them. This should include both the Dify platform itself and any custom code or integrations.
  • Inventory and Monitoring:
  • Maintain an accurate inventory of all deployed AI applications and Dify instances. Continuously monitor these systems for suspicious activities, configuration changes, and unauthorized data access attempts, applying the same level of scrutiny as for any other business-critical enterprise system.
  • Access Control Review:
  • Periodically review and enforce stringent access control policies for Dify accounts and API keys. Ensure that least privilege principles are applied to all users and services interacting with the Dify platform.

Technical Takeaways

  • The DifyTap vulnerabilities show the critical need for strong authorization and access control mechanisms within AI orchestration platforms, extending to tracing functionalities and internal APIs.
  • AI platforms, due to their proximity to highly sensitive data like chat histories, user prompts, and proprietary documents, require security assessments and monitoring comparable to any internet-facing, business-critical technology.
  • Even vulnerabilities with limited immediate impact, such as CVE-2026-41948, are significant architectural flaws that could become more severe with future platform developments or the discovery of chained exploits.
  • Proactive implementation of Web Application Firewall (WAF) rules and continuous monitoring for anomalous behavior in API calls and data access patterns are important for addressing emergent threats in AI-centric environments.
  • The aggregation of seemingly distinct vulnerabilities can lead to cumulative "wiretapping" effects, which allows for persistent data exfiltration channels that bypass standard security controls.