CVE-2026-5757: Ollama Information Disclosure Vulnerability

CVE-2026-5757 Overview

CVE-2026-5757 is an unauthenticated remote information disclosure vulnerability in Ollama, an open-source large language model (LLM) runtime. The flaw resides in the model quantization engine and allows a network attacker to read the server's heap memory without any authentication or user interaction. Exfiltrated memory contents may include model data, session artifacts, API tokens, or other sensitive material resident in the process address space. The vulnerability is classified under CWE-125 (Out-of-Bounds Read) and is tracked by CERT/CC in Vulnerability Note VU#518910.

Critical Impact
Unauthenticated attackers can remotely read heap memory from an Ollama server, enabling exposure of sensitive data, follow-on compromise, and stealthy persistence.

Affected Products

Ollama (all versions prior to the vendor-supplied fix)
Deployments exposing the Ollama HTTP API to untrusted networks
Self-hosted LLM inference stacks that invoke the quantization engine

Discovery Timeline

2026-06-26 - CVE-2026-5757 published to NVD
2026-06-29 - Last updated in NVD database

Technical Details for CVE-2026-5757

Vulnerability Analysis

The vulnerability exists in the quantization pipeline that Ollama uses to convert and load model weights. During quantization, the engine reads structured tensor data from an input source and processes it using length or offset fields embedded in that data. When these fields are not validated against the true bounds of the source buffer, the engine reads beyond the allocated region and returns the resulting bytes to the caller.

Because the endpoint responsible for triggering the code path is reachable over the network without authentication, an attacker can repeatedly issue crafted requests and reconstruct fragments of the server's heap. Sensitive material co-located on the heap, such as loaded model tensors, cached prompts, environment-derived secrets, or session state, can be extracted incrementally.

Root Cause

The root cause is an out-of-bounds read [CWE-125] within the model quantization engine. Length or index values controlled by attacker-supplied model or tensor input are used to compute read positions without adequate bounds checking. The engine then dereferences memory outside the intended buffer and includes the resulting bytes in its response or logged output.

Attack Vector

The attack vector is network-based and requires no privileges or user interaction. An attacker who can reach the Ollama service submits a malformed model or tensor artifact that drives the quantization engine down the vulnerable code path. The server responds with data containing bytes read from adjacent heap regions. Repeated, parameterized requests allow the attacker to sweep memory and reassemble sensitive content offline. Technical specifics are documented in the CERT/CC advisory VU#518910.

Detection Methods for CVE-2026-5757

Indicators of Compromise

Repeated inbound requests to Ollama model-loading or quantization endpoints from a small set of external source addresses.
Anomalous model or tensor upload artifacts with malformed headers, oversized length fields, or unusual quantization parameters.
Ollama process logs showing repeated quantization errors, parse failures, or truncated tensor reads.
Outbound responses from the Ollama service that are unusually large relative to the request size.

Detection Strategies

Alert on high-frequency access to Ollama API paths handling model import, conversion, or quantization from non-administrative sources.
Baseline normal request and response sizes for the Ollama service, then flag statistical outliers indicative of memory scraping.
Correlate quantization error log entries with the source IP and request identifier to surface iterative probing behavior.

Monitoring Recommendations

Forward Ollama application logs and reverse-proxy access logs to a centralized analytics platform for retention and correlation.
Monitor the Ollama process for abnormal memory growth, crashes, or repeated worker restarts triggered by malformed inputs.
Track network exposure of Ollama listeners and alert when the service is reachable from untrusted network segments.

How to Mitigate CVE-2026-5757

Immediate Actions Required

Restrict network access to the Ollama service so that only trusted clients and internal applications can reach the API.
Place Ollama behind an authenticating reverse proxy that enforces identity, rate limits, and request size caps.
Disable or gate model import and quantization endpoints on production instances until a patched version is deployed.
Rotate any secrets, API keys, or tokens that were loaded into the Ollama process environment while the service was exposed.

Patch Information

At the time of publication, no vendor advisory URL is listed in the NVD entry for CVE-2026-5757. Administrators should monitor the Ollama project site and the CERT/CC note VU#518910 for the fixed release and upgrade to the patched version once available. Verify the running version after upgrade and confirm that the quantization code path rejects malformed tensor inputs.

Workarounds

Bind the Ollama listener to 127.0.0.1 or a private interface rather than 0.0.0.0 on internet-reachable hosts.
Enforce network segmentation and firewall rules that block inbound access to the Ollama API from untrusted zones.
Reject externally supplied model files and permit only vetted, internally hosted models for loading and quantization.
Deploy Web Application Firewall (WAF) rules that drop requests carrying malformed tensor headers or oversized length fields.

bash

# Configuration example: bind Ollama to loopback and restrict inbound access
export OLLAMA_HOST=127.0.0.1:11434
systemctl restart ollama

# Example nftables rule to block external access to the Ollama port
nft add rule inet filter input tcp dport 11434 ip saddr != 10.0.0.0/8 drop