A critical, unpatched vulnerability in Ollama’s GGUF model quantization engine, tracked as CVE-2026-5757, allows unauthenticated attackers to remotely exfiltrate sensitive heap memory from servers running the popular open-source LLM platform, with no vendor patch currently available.
Ollama is one of the most widely adopted open-source platforms for running large language models (LLMs) locally, supporting environments across macOS, Windows, and Linux.
With over 155,000 GitHub stars and adoption spanning developers, AI researchers, and enterprise deployments, Ollama has become a foundational tool for running models like Meta’s Llama 4, DeepSeek-R1, and Google’s Gemma3 without depending on external cloud APIs.
Its growing footprint in production and research environments makes security vulnerabilities within the platform a high-priority concern for the broader AI and security community.
CVE-2026-5757 : Ollama Vulnerability
Disclosed on April 22, 2026, Vulnerability Note VU#518910, CVE-2026-5757 is an unauthenticated remote information disclosure vulnerability residing in Ollama’s model quantization engine.
The flaw enables a remote attacker without any prior authentication to upload a specially crafted GGUF (GPT-Generated Unified Format) file and trigger the quantization process, causing the server to read beyond its intended memory boundaries and write the leaked data into a new model layer.
The vulnerability stems from three compounding weaknesses in Ollama’s codebase, each amplifying the others:
- No Bounds Checking: The quantization engine directly trusts tensor metadata, specifically the element count embedded in the user-supplied GGUF file header, without verifying it against the actual size of the data buffer provided.
- Unsafe Memory Access via
unsafe.Slice: Go’sunsafe.Slicefunction is used to construct a memory slice based on the attacker-controlled element count. Because no bounds validation is performed, this slice can extend far beyond the legitimate data buffer and directly into the server application’s heap. - Built-In Data Exfiltration Path: The quantization engine inadvertently processes and writes heap data out-of-bounds to a new model layer. An attacker can then invoke Ollama’s registry API to “push” this crafted layer to an attacker-controlled external server, completing a stealthy, silent exfiltration of raw heap contents.
This three-stage attack chain, malicious upload, memory boundary violation, and registry-based exfiltration, makes CVE-2026-5757 particularly dangerous because it weaponizes Ollama’s own legitimate API functionality to exfiltrate stolen data.
CVE-2026-5757 is rated High by Feedly AI, and the vulnerability has wide-reaching consequences for affected deployments. Successful exploitation can result in:
- Sensitive data exposure heap memory may contain API keys, authentication tokens, model weights, and runtime configuration data processed in-memory
- Lateral movement and further compromise of leaked credentials or internal network details can enable attackers to pivot deeper into the infrastructure.
- Stealthy persistence via the legitimate push API makes detection by conventional security tools significantly harder.
This is not the first time Ollama has faced serious security scrutiny. NSFOCUS previously flagged a separate unauthorized access vulnerability (CNVD-2025-04094) stemming from Ollama’s default lack of authentication on port 11434, allowing unauthenticated API access on internet-exposed instances.
Earlier, Security researchers disclosed six critical Ollama vulnerabilities, including model theft, DoS, and model tampering, some of which remained unpatched, with maintainers advising WAF- or proxy-based mitigation.
No Patch
According to the CERT/CC advisory, coordinated disclosure was attempted. Still, the Ollama vendor could not be reached before publication, so no official patch exists at the time of this writing. Vendor status for Ollama AI is currently listed as “Unknown.”
The underlying fix requires implementing proper bounds checking to ensure tensor metadata from user-supplied GGUF files is validated against the actual data buffer size before any memory operations are performed.
Mitigation
Until an official patch is released, organizations running Ollama in any environment, particularly those with internet-exposed or multi-user deployments, should immediately apply the following interim mitigations:
- Restrict or turn off model upload functionality in all environments exposed to untrusted users or external networks
- Limit Ollama deployments to local or internally trusted networks, avoiding any public internet exposure of port 11434
- Accept models only from verified, trusted sources and implement validation controls on uploaded model files
- Deploy WAF rules or reverse proxy authentication to add an access control layer in front of the Ollama API interface
- Monitor registry API push activity for anomalous outbound model transfers to unknown external hosts
CVE-2026-5757 is emblematic of an accelerating trend: as AI infrastructure tools like Ollama become mission-critical in enterprise and research environments, they become high-value targets for adversaries.
The research team had previously identified a critical Out-of-Bounds Write in Ollama’s GGUF parsing, affecting all versions before 0.7.0, underscoring that file-format parsing is a recurring and systemic weakness across the platform.
Security teams must now treat LLM platforms with the same rigor applied to web servers and databases requiring authentication, input validation, and network segmentation as baseline controls.
| Attribute | Details |
|---|---|
| CVE ID | CVE-2026-5757 |
| Affected Component | Ollama GGUF Quantization Engine |
| Attack Type | Out-of-Bounds Heap Read/Write + Memory Exfiltration |
| Authentication Required | None (Unauthenticated) |
| Patch Available | No |
| Disclosure Date | April 22, 2026 |
| Severity | High |
FAQ
Q1: What is CVE-2026-5757?
CVE-2026-5757 is an unpatched, unauthenticated remote heap memory exfiltration vulnerability in Ollama’s GGUF model quantization engine, disclosed by CERT/CC on April 22, 2026.
Q2: Is a patch available for CVE-2026-5757?
No patch is currently available; CERT/CC was unable to reach the Ollama vendor for coordinated disclosure, making this an active zero-day risk.
Q3: How does an attacker exploit this Ollama vulnerability?
An attacker uploads a maliciously crafted GGUF file to trigger quantization, causing Go’s unsafe.Slice to read beyond the heap buffer, then uses Ollama’s push API to exfiltrate the leaked memory to an attacker-controlled server.
Q4: How can organizations protect their Ollama deployments right now?
Organizations should immediately restrict model upload access, isolate Ollama to trusted internal networks, deploy reverse proxy authentication, and monitor for unusual registry push activity to external hosts.
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