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CVE-2026-108836 represents a critical server-side request forgery vulnerability discovered within the popular Ollama artificial intelligence platform.
The security flaw specifically targets versions up to 0.34.1 of the software package.
The root cause of this vulnerability lies deep inside the Pull API functionality of the codebase.
More precisely, the defect resides within the IsValid function located in the types/model/name.go source file.
During model retrieval operations, the application accepts user-supplied input to identify the target model resource.
However, the IsValid validation routine fails to properly inspect and sanitize these incoming model name strings.
Because of this insufficient validation, a remote malicious actor can manipulate the target URL structure.
An attacker is thus able to supply crafted inputs that bypass standard string checking mechanisms entirely.
When the Ollama server processes this malicious model pull request, it attempts to fetch the specified resource.
Rather than retrieving a legitimate model file from an approved repository, the server makes unintended network requests.
These requests can be directed toward internal network resources that are normally shielded from external access.
Alternatively, they can target attacker-controlled infrastructure on the open internet for data exfiltration.
This behavior directly exemplifies a classic Server-Side Request Forgery attack pattern mapped to CWE-918.
The severity of this issue is amplified because it can be initiated completely remotely without prior authentication.
An attacker does not need valid credentials or local system access to trigger the flaw.
By exploiting this vector, malicious actors can perform comprehensive internal network reconnaissance scans.
They can interact with internal web services, administrative panels, and microservices running behind firewalls.
Furthermore, attackers can target cloud metadata service endpoints, such as those exposed by AWS EC2 instances.
Accessing these metadata endpoints allows unauthorized entities to steal temporary IAM credentials and access tokens.
The core danger stems from the fact that requests originate from the trusted server IP address itself.
Perimeter security controls and firewalls implicitly trust requests originating from internal machine components.
Consequently, traditional boundary defenses fail to block these forged internal requests.
Public disclosure of exploit techniques has accelerated the likelihood of active exploitation in production environments.
Automated threat actors constantly scan for instances running unpatched versions of the Ollama platform.
Developers and system administrators maintaining local or enterprise AI stacks must treat this flaw with utmost urgency.
The vulnerability compromises the integrity and confidentiality of the host network environment.
Without proper input validation, any application handling external model pulling remains dangerously exposed.
Resolving this defect requires rigorous enforcement of strict URL allow-lists and robust protocol checks.
Fortunately, the maintainers quickly responded by developing a targeted security patch to close the loophole.
Upgrading immediately to the designated safe release version completely mitigates the underlying attack vector.
DailyCVE Form:
Platform: Ollama AI platform
Version: Up to 0.34.1
Vulnerability: Server-side request forgery
Severity: Critical security flaw
date: October 12 2026
Prediction: Already patched today
What Undercode Say
Showing bash commands and codes related to the blog
Check installed Ollama version ollama --version Upgrade Ollama to fixed version 0.34.2 curl -fsSL https://ollama.com/install.sh | sh
Exploit: (Educational Purposes!)
An attacker initiates a malicious model pull command containing an arbitrary or internal URL instead of a valid model name, bypassing the IsValid check in types/model/name.go to trigger unauthorized server-side requests.
Protection: from this CVE
Upgrade the Ollama software package immediately to version 0.34.2 or later, which incorporates the official patch (commit dfabde4) enforcing strict URL and name validation.
Impact:
Allows remote attackers to perform server-side request forgery, enabling internal network scanning, access to protected microservices, and potential theft of cloud instance metadata credentials.
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Sources:
Reported By: github.com
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