Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:NSummary
CVE-2023-49785 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Nextchat Nextchat. Its CVSS base score is 9.1 (Critical).
Operationally, ranked in the top 0.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SC-7 (Boundary Protection) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
NextChat, also known as ChatGPT-Next-Web, is a cross-platform chat user interface for ChatGPT. Versions 2.11.2 and earlier contain server-side request forgery and cross-site scripting flaws tracked under CVE-2023-49785. The issues permit unauthenticated network callers to reach internal HTTP resources with both read and write methods, including POST and PUT, while also enabling reflected or stored script execution.
An attacker with network access to an exposed instance can issue crafted requests that reach otherwise unreachable internal endpoints, exfiltrate data, modify state on those systems, or relay arbitrary traffic to external targets to obscure the true source IP. Because the application requires no credentials for these proxy-like behaviors, the flaws can be triggered remotely without user interaction.
Public references, including the project repository and associated issue and pull-request threads, indicate that no official patch existed at disclosure. Recommended mitigations center on avoiding public internet exposure entirely or placing the service in a strictly isolated network segment that has no routes to other internal assets.
The vulnerability affects an AI-oriented chat frontend and carries a CVSS score of 9.1. Its EPSS score has reached a peak of 0.9338 with a current value of 0.9044, indicating sustained exploitation interest after publication.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2633
Vulnerability Data
NextChat, also known as ChatGPT-Next-Web, is a cross-platform chat user interface for use with ChatGPT. Versions 2.11.2 and prior are vulnerable to server-side request forgery and cross-site scripting. This vulnerability enables read access to internal HTTP endpoints but also write…
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access using HTTP POST, PUT, and other methods. Attackers can also use this vulnerability to mask their source IP by forwarding malicious traffic intended for other Internet targets through these open proxies. As of time of publication, no patch is available, but other mitigation strategies are available. Users may avoid exposing the application to the public internet or, if exposing the application to the internet, ensure it is an isolated network with no access to any other internal resources.
- CWE(s)
Related Threats
Likely ATT&CK TechniquesAI
Techniques this vulnerability likely enables, inferred from its description, weakness type, and attributed-actor tradecraft. Confidence is per-technique.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Enforces network boundary controls and isolation so the exposed NextChat instance cannot reach internal HTTP endpoints or act as an open proxy.
Information flow enforcement policies can explicitly deny the unauthorized read/write flows to internal resources that the SSRF vulnerability enables.
Input validation on user-supplied URLs can reject or sanitize requests that would otherwise trigger SSRF or reflected XSS against internal or external targets.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.