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HIGH severity

Pydantic AI has Server-Side Request Forgery (SSRF) in URL Download HandlingGHSA-2jrp-274c-jhv3

HIGHFix: pydantic/pydantic-ai@d398bc9

GHSA-2jrp-274c-jhv3 is a high-severity (CVSS 8.6) Server-Side Request Forgery (SSRF) vulnerability in pydantic-ai. A fix is available for pydantic-ai — see the affected versions and patch details below.

Also known asCVE-2026-25580PYSEC-2026-2976PYSEC-2026-2980
Published
Updated
Affected
2 pkgs
Patched
2 / 2
Exploits
None indexed
Exploitation data as of Nov 8, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for GHSA-2jrp-274c-jhv3.

EPSS Exploitation Probability

via FIRST.org ↗
0.6%probability of exploitation in next 30 days
Lower Risk-0.01%
Lower risk than most CVEs51th percentile — riskier than 51% of all scored CVEsHighest risk
0.00%0.39%0.78%1.17%0.0%0.6%May 26Sep 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

2 pkgs affected
🐍pydantic-ai🐍pydantic-ai-slim

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Summary

A Server-Side Request Forgery (SSRF) vulnerability exists in Pydantic AI's URL download functionality. When applications accept message history from untrusted sources, attackers can include malicious URLs that cause the server to make HTTP requests to internal network resources, potentially accessing internal services or cloud credentials.

This vulnerability only affects applications that accept message history from external users, such as those using:

  • Agent.to_web or clai web to serve a chat interface
  • VercelAIAdapter for Vercel AI SDK integration
  • AGUIAdapter or Agent.to_ag_ui for AG-UI protocol integration
  • Custom APIs that accept message history from user input

Applications that only use hardcoded or developer-controlled URLs are not affected.

Description

The download_item() helper function downloads content from URLs without validating that the target is a public internet address. When user-supplied message history contains URLs, attackers can:

  1. Access internal services: Request http://127.0.0.1, localhost, or private IP ranges (10.x.x.x, 172.16.x.x, 192.168.x.x)
  2. Steal cloud credentials: Access cloud metadata endpoints (AWS IMDSv1 at 169.254.169.254, GCP, Azure, Alibaba Cloud)
  3. Scan internal networks: Enumerate internal hosts and ports

Who Is Affected

You are affected if your application:

  1. Uses Agent.to_web or clai web - The web interface accepts file attachments via the Vercel AI Data Stream Protocol, where users can provide arbitrary URLs through chat messages.

  2. Uses VercelAIAdapter - Chat interfaces built with Vercel AI SDK allow users to submit messages containing URLs that are processed server-side.

  3. Uses AGUIAdapter or Agent.to_ag_ui - The AG-UI protocol allows users to provide file references with URLs as part of agent interactions.

  4. Exposes a custom API accepting message history - Any endpoint that accepts message history or ImageUrl, AudioUrl, VideoUrl, DocumentUrl objects from user input.

Attack Scenario

Via chat interface, an attacker submits a message with a file attachment pointing to an internal resource:

{
  "role": "user",
  "parts": [
    {"type": "file", "mediaType": "image/png", "url": "http://169.254.169.254/latest/meta-data/iam/security-credentials/"}
  ]
}

Affected Model Integrations

Multiple model integrations download URL content in certain conditions:

ProviderDownloaded Types
OpenAIChatModelAudioUrl, DocumentUrl
AnthropicModelDocumentUrl (text/plain)
GoogleModel (GLA)All URL types (except YouTube and Files API URLs)
XaiModelDocumentUrl
BedrockConverseModelImageUrl, DocumentUrl, VideoUrl (non-S3 URLs)
OpenRouterModelAudioUrl

Remediation

Upgrade to Patched Version

Upgrade to the patched version or later. The fix adds comprehensive SSRF protection:

  • Blocks private/internal IP addresses by default
  • Always blocks cloud metadata endpoints (even with allow-local)
  • Only allows http:// and https:// protocols
  • Resolves hostnames before requests to prevent DNS rebinding
  • Validates each redirect target

New force_download='allow-local' Option

If an application legitimately needs to access local/private network resources (e.g., in a fully trusted internal environment), it can explicitly opt in:

from pydantic_ai import ImageUrl

# Default behavior: private IPs are blocked
ImageUrl(url="http://internal-service/image.png")  # Raises ValueError

# Opt-in to allow local access (use with caution)
ImageUrl(url="http://internal-service/image.png", force_download='allow-local')

Important: Cloud metadata endpoints (169.254.169.254, fd00:ec2::254, 100.100.100.200) are always blocked, even with allow-local.

Workaround for Older Versions

If a project cannot upgrade immediately, use a history processor to filter out URLs targeting local/private addresses:

import ipaddress
import socket
from urllib.parse import urlparse

from pydantic_ai import Agent, ModelMessage, ModelRequest
from pydantic_ai.messages import AudioUrl, DocumentUrl, ImageUrl, VideoUrl

def is_private_url(url: str) -> bool:
    """Check if a URL targets a private/internal IP address."""
    try:
        parsed = urlparse(url)
        hostname = parsed.hostname
        if not hostname:
            return True  # Invalid URL, block it

        # Resolve hostname to IP
        ip_str = socket.gethostbyname(hostname)
        ip = ipaddress.ip_address(ip_str)

        # Block private, loopback, and link-local addresses
        return ip.is_private or ip.is_loopback or ip.is_link_local
    except (socket.gaierror, ValueError):
        return True  # DNS resolution failed, block it

def filter_private_urls(messages: list[ModelMessage]) -> list[ModelMessage]:
    """Remove URL parts that target private/internal addresses."""
    url_types = (ImageUrl, AudioUrl, VideoUrl, DocumentUrl)
    filtered = []
    for msg in messages:
        if isinstance(msg, ModelRequest):
            safe_parts = [
                part for part in msg.parts
                if not (isinstance(part, url_types) and is_private_url(part.url))
            ]
            if safe_parts:
                filtered.append(ModelRequest(parts=safe_parts))
        else:
            filtered.append(msg)
    return filtered

# Apply the filter to your agent
agent = Agent('openai:gpt-5', history_processors=[filter_private_urls])

Technical Details of the Fix

The fix introduces a new _ssrf.py module with comprehensive protection:

  1. Protocol validation: Only http:// and https:// allowed
  2. DNS resolution before request: Prevents DNS rebinding attacks
  3. Private IP blocking (by default):
    • 127.0.0.0/8, ::1/128 (loopback)
    • 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16 (private)
    • 169.254.0.0/16, fe80::/10 (link-local)
    • 100.64.0.0/10 (CGNAT)
    • fc00::/7 (unique local)
    • 2002::/16 (6to4, can embed private IPv4)
  4. Cloud metadata always blocked: 169.254.169.254, fd00:ec2::254, 100.100.100.200
  5. Safe redirect handling: Each redirect validated before following (max 10)

Affected Packages

2 total 2 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIpydantic-ai≥ 0.0.26&&< 1.56.01.56.0pip install --upgrade 'pydantic-ai==1.56.0'
🐍PyPIpydantic-ai-slim≥ 0.0.26&&< 1.56.01.56.0pip install --upgrade 'pydantic-ai-slim==1.56.0'

Affected Products

1 product · 1 configurations
Application
pydantic aipydantic
≥ 0.0.26 && < 1.56.0
range

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for pydantic-ai, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update pydantic-ai to 1.56.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-2jrp-274c-jhv3 is resolved across your whole dependency graph.

  3. Workarounds

    Restrict outbound requests from the affected component to an allowlist of hosts, block access to link-local and internal address ranges at the network layer, and require authentication on internal services so a forged request cannot reach them unauthenticated.

Fixing This On Your OS

If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.

Red HatImportant

This IMPORTANT vulnerability affects Red Hat Enterprise Linux AI products using Pydantic AI when applications process untrusted message history. A Server-Side Request Forgery (SSRF) flaw in URL download handling allows attackers to inject malicious URLs, potentially accessing internal network resources or cloud…

Workaround published by Red Hat
To mitigate, configure applications using Pydantic AI to avoid accepting message history from untrusted external sources. Implement robust input validation and sanitization for all URLs processed by the application. Additionally, restrict network access for the Pydantic AI application to only essential internal and external resources, thereby limiting the potential impact of SSRF attacks.
Source: Red Hat security advisory for GHSA-2jrp-274c-jhv3 (CC BY 4.0)

Frequently Asked Questions

## Summary A Server-Side Request Forgery (SSRF) vulnerability exists in Pydantic AI's URL download functionality. When applications accept message history from untrusted sources, attackers can include malicious URLs that cause the server to make HTTP requests to internal network resources, potentially accessing internal services or cloud credentials. **This vulnerability only affects applications that accept message history from external users**, such as those using: - **`Agent.to_web`** or **`clai web`** to serve a chat interface - **`VercelAIAdapter`** for Vercel AI SDK integration - **`AG
O3 Security · Impact-Aware SCA

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