CVE-2026-25580 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.
Pydantic AI Affected by Server-Side Request Forgery (SSRF) in URL Download Handling
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 CVE-2026-25580.
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
CVE-2026-25580 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 377,636 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
Real-World Exposure
pydantic-ai🐍pydantic-ai-slimReal-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_weborclai webto serve a chat interfaceVercelAIAdapterfor Vercel AI SDK integrationAGUIAdapterorAgent.to_ag_uifor 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:
- 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) - Steal cloud credentials: Access cloud metadata endpoints (AWS IMDSv1 at
169.254.169.254, GCP, Azure, Alibaba Cloud) - Scan internal networks: Enumerate internal hosts and ports
Who Is Affected
You are affected if your application:
-
Uses
Agent.to_weborclai web- The web interface accepts file attachments via the Vercel AI Data Stream Protocol, where users can provide arbitrary URLs through chat messages. -
Uses
VercelAIAdapter- Chat interfaces built with Vercel AI SDK allow users to submit messages containing URLs that are processed server-side. -
Uses
AGUIAdapterorAgent.to_ag_ui- The AG-UI protocol allows users to provide file references with URLs as part of agent interactions. -
Exposes a custom API accepting message history - Any endpoint that accepts message history or
ImageUrl,AudioUrl,VideoUrl,DocumentUrlobjects 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:
| Provider | Downloaded Types |
|---|---|
OpenAIChatModel | AudioUrl, DocumentUrl |
AnthropicModel | DocumentUrl (text/plain) |
GoogleModel (GLA) | All URL types (except YouTube and Files API URLs) |
XaiModel | DocumentUrl |
BedrockConverseModel | ImageUrl, DocumentUrl, VideoUrl (non-S3 URLs) |
OpenRouterModel | AudioUrl |
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://andhttps://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:
- Protocol validation: Only
http://andhttps://allowed - DNS resolution before request: Prevents DNS rebinding attacks
- 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)
- Cloud metadata always blocked:
169.254.169.254,fd00:ec2::254,100.100.100.200 - Safe redirect handling: Each redirect validated before following (max 10)
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | pydantic-ai | ≥ 0.0.26&&< 1.56.0 | 1.56.0pip install --upgrade 'pydantic-ai==1.56.0' |
| 🐍PyPI | pydantic-ai-slim | ≥ 0.0.26&&< 1.56.0 | 1.56.0pip install --upgrade 'pydantic-ai-slim==1.56.0' |
Detection & mitigation playbook
Open-source dependencyDetect
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.
Fix
Update pydantic-ai to 1.56.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-25580 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2026-25580 can be triaged on real exposure rather than presence alone.
Tailored to CVE-2026-25580. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is CVE-2026-25580 in your dependencies?
O3 Security finds CVE-2026-25580 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.