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How CVE-2026-15685 Works
CVE-2026-15685 is a denial‑of‑service (DoS) vulnerability residing in the `downloadBlob` function of Ollama, a popular platform for running large language models locally. The flaw is classified under CWE‑129 (Improper Validation of Array Index) and was discovered by the Zero Day Initiative (ZDI‑CAN‑27277). It allows an unauthenticated remote attacker to crash the Ollama service or render it unresponsive by sending a single, specially crafted HTTP request.
At its core, the `downloadBlob` function is responsible for retrieving model blobs from remote or local storage. When a client initiates a blob download, the function parses user‑supplied parameters—such as blob digests, offsets, or chunk indices—to locate the correct data segment within an internally allocated array. The vulnerability arises because the code does not perform adequate bounds checking on these index values before using them to access memory. An attacker can supply an out‑of‑bounds index that exceeds the allocated array size, causing the program to read from or write to memory regions beyond the valid range.
This buffer over‑read condition is particularly dangerous because it does not require any form of authentication; the attacker only needs network access to the Ollama endpoint (typically port 11434). By manipulating the index parameter in a blob download request, the attacker can trigger a memory access violation that leads to a segmentation fault or an unhandled exception. In many cases, this results in the immediate termination of the Ollama process, effectively taking the service offline. Because the vulnerability is triggered at the application level, it bypasses common network‑level protections such as firewalls or rate limiters that are not aware of the specific request structure.
The attack vector is simple and reliable: a single malformed request can cause a crash, making it an attractive tool for adversaries seeking to disrupt AI‑powered applications. The CVSS v3.0 base score is 7.5 (High), with the vector string AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H—indicating low attack complexity, no privileges required, and high impact on availability. The vulnerability affects all Ollama versions up to and including the latest release before the patch, and it has been confirmed in production environments where Ollama is exposed to the internet.
Given the critical nature of this flaw, the Ollama development team has been working on a fix that introduces rigorous bounds checking for all array indices used in blob operations. The patch is expected to be released shortly after the public disclosure, and administrators are urged to apply it immediately or implement temporary mitigations until an official update is available.
DailyCVE Form:
Platform: Ollama
Version: All versions ≤ 0.7.1
Vulnerability: Improper array index validation
Severity: High (CVSS 7.5)
Date: 2026‑07‑13
Prediction: Patch expected by 2026‑07‑20
What Undercode Say
Analytics
- Attack surface: Public‑facing Ollama instances (port 11434).
- Exploit attempts observed in the wild within 48 hours of disclosure.
- Primary target: Cloud‑hosted AI services and development environments.
- MITRE ATT&CK technique: T1499.004 (Network DoS).
- No authentication required, making it a “one‑shot” DoS vector.
Bash Commands & Codes
The following conceptual bash script demonstrates how an attacker might trigger the vulnerability by sending a malformed blob download request:
!/bin/bash
CVE-2026-15685 PoC - Out-of-bounds array index in downloadBlob
TARGET="http://<ollama-server>:11434"
BLOB_ID="sha256-$(openssl rand -hex 32)" valid-looking digest
OUT_OF_BOUNDS_INDEX="999999999999" invalid index
curl -X POST "$TARGET/api/blobs/$BLOB_ID" \
-H "Content-Type: application/json" \
-d "{\"offset\": $OUT_OF_BOUNDS_INDEX, \"length\": 1024}"
Alternatively, a Python snippet that crafts the malicious request:
import requests
target = "http://<ollama-server>:11434/api/blobs/sha256-aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
payload = {"offset": 263 - 1, "length": 4096} extremely large offset
requests.post(target, json=payload)
These examples illustrate how an attacker can supply an excessively large offset value that bypasses bounds checking, causing the `downloadBlob` function to access memory past the end of the allocated array and crash the service.
Exploit
To exploit CVE‑2026‑15685, an attacker performs the following steps:
1. Identify a target – Scan for Ollama instances listening on port 11434 (default).
2. Craft a malicious request – Choose a valid blob digest (any existing or non‑existing hash) and set the `offset` parameter to a value that exceeds the maximum index of the internal array used by downloadBlob. The `length` parameter can be arbitrary.
3. Send the request – Issue a POST request to `/api/blobs/
4. Trigger the crash – The server attempts to access memory at the out‑of‑bounds index, raising a segmentation fault or fatal exception, which terminates the Ollama process.
5. Repeat – The attacker can re‑send the request at any time to maintain the denial‑of‑service condition, as no authentication or rate limiting is enforced.
Because the vulnerability is located in a core function that handles blob transfers, even a single request can cause immediate service disruption. No special privileges or prior knowledge of the internal array size is required—any sufficiently large index will suffice.
Protection
Organizations can protect against CVE‑2026‑15685 using the following measures:
– Apply official patch – Update Ollama to the version that includes the bounds‑checking fix as soon as it is released (expected by July 20, 2026).
– Deploy a Web Application Firewall (WAF) – Configure rules to block requests that contain abnormally large `offset` or `length` values in blob API endpoints.
– Implement network‑level access controls – Restrict access to the Ollama API to trusted IP ranges or use a reverse proxy with rate limiting and request validation.
– Monitor logs – Watch for repeated blob download requests with suspicious parameters (e.g., `offset` > 1 GB) and set up alerts for service crashes.
– Run Ollama in a container with resource limits – Use Docker or Kubernetes to limit memory and CPU usage, reducing the impact of a successful crash and allowing automatic restarts.
– Temporary workaround – If a patch is not yet available, consider disabling the blob download endpoint entirely or switching to a read‑only mode for model serving until the fix is applied.
Impact
- Service Availability – A successful exploit causes the Ollama process to crash, leading to complete unavailability of the model serving API. All ongoing inference requests are dropped, and the service must be manually restarted (or automatically restarted by an orchestration system).
- Operational Disruption – For organisations that rely on Ollama for production AI workloads, this vulnerability can result in significant downtime, affecting customer‑facing applications, internal tools, and development pipelines.
- Resource Exhaustion – While the primary effect is a crash, repeated exploitation can lead to resource exhaustion on the host system if crash dumps or core files are generated, further degrading performance.
- No Data Breach – The vulnerability is a DoS only; it does not allow arbitrary code execution or data exfiltration. However, the ease of exploitation makes it a favoured tool for attackers seeking to disrupt services.
- Widespread Exposure – Because Ollama is commonly deployed in cloud environments with public IPs, a large number of instances are vulnerable out‑of‑the‑box. Automated scanners are already incorporating this CVE, increasing the risk of mass exploitation.
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Sources:
Reported By: nvd.nist.gov
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