Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:C/C:N/I:L/A:NSummary
CVE-2026-33619 is a medium-severity SSRF (CWE-918) vulnerability in Pinchtab Pinchtab. Its CVSS base score is 4.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 16th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16400
Vulnerability Data
PinchTab is a standalone HTTP server that gives AI agents direct control over a Chrome browser. PinchTab v0.8.3 contains a server-side request forgery issue in the optional scheduler's webhook delivery path. When a task is submitted to `POST /tasks` with…
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a user-controlled `callbackUrl`, the v0.8.3 scheduler sends an outbound HTTP `POST` to that URL when the task reaches a terminal state. In that release, the webhook path validated only the URL scheme and did not reject loopback, private, link-local, or other non-public destinations. Because the v0.8.3 implementation also used the default HTTP client behavior, redirects were followed and the destination was not pinned to validated IPs. This allowed blind SSRF from the PinchTab server to attacker-chosen HTTP(S) targets reachable from the server. This issue is narrower than a general unauthenticated internet-facing SSRF. The scheduler is optional and off by default, and in token-protected deployments the attacker must already be able to submit tasks using the server's master API token. In PinchTab's intended deployment model, that token represents administrative control rather than a low-privilege role. Tokenless deployments lower the barrier further, but that is a separate insecure configuration state rather than impact created by the webhook bug itself. PinchTab's default deployment model is local-first and user-controlled, with loopback bind and token-based access in the recommended setup. That lowers practical risk in default use, even though it does not remove the underlying webhook issue when the scheduler is enabled and reachable. This was addressed in v0.8.4 by validating callback targets before dispatch, rejecting non-public IP ranges, pinning delivery to validated IPs, disabling redirect following, and validating `callbackUrl` during task submission.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
SSRF in optional public-facing HTTP scheduler component directly enables exploitation of the server via attacker-controlled outbound requests.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation of the user-supplied callbackUrl to reject non-public, loopback, or private destinations before any outbound webhook is issued.
Enforces information flow policies that can explicitly block or restrict outbound HTTP requests to unapproved address ranges or follow-on redirects.
Boundary protection mechanisms can be configured to deny the server-initiated connections to internal or non-public networks that the SSRF attempts to reach.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
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.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.