Ollama: Hackers Exploit Ollama Model Uploads to Leak Server Data

Critical Unpatched Vulnerability in Ollama Exposes Sensitive Data to Attackers

Cybersecurity researchers have identified a severe, unpatched vulnerability in Ollama, a widely used open-source platform for running large language models (LLMs) locally. Tracked as CVE-2026-5757, the flaw resides in Ollama’s model quantization engine and allows unauthenticated attackers to steal sensitive server data by uploading a maliciously crafted AI model file.

How the Exploit Works

Ollama’s quantization process designed to optimize model performance by reducing numerical precision contains an out-of-bounds memory vulnerability in its handling of GPT-Generated Unified Format (GGUF) files. When an attacker uploads a specially crafted GGUF file and triggers quantization, the engine reads beyond safe memory limits due to three critical flaws:

1. Unchecked file metadata – The engine trusts user-provided metadata without verifying its alignment with the actual data size.
2. Unsafe memory operations – A Go-based memory slice extends into the application’s heap, enabling unauthorized access.
3. Data exfiltration via API – Stolen memory (including sensitive data) is written to a new model layer and can be extracted through Ollama’s registry API.

Potential Impact

Since the vulnerability grants access to the server’s heap memory, attackers can silently extract highly sensitive data processed during normal operations, including:

• API keys
• Private user data
• Proprietary intellectual property

Worse, the exploit could enable full server compromise, allowing attackers to move laterally within a network, establish persistence, and evade detection by standard security tools.

Discovery & Current Status

The flaw was uncovered by security researcher Jeremy Brown, who employed AI-assisted vulnerability research techniques. As of late April 2026, the CERT Coordination Center has been unable to contact Ollama’s vendor, leaving the vulnerability unpatched.

Mitigation Measures

Until an official fix is released, organizations running Ollama are advised to:

• Disable or restrict model upload functionality on exposed servers.
• Limit deployments to isolated or trusted networks.
• Only use AI models from verified sources.
• Enforce strict network controls to block unauthorized data exfiltration.

The incident underscores the growing risks of supply chain attacks in AI infrastructure, particularly in open-source tools with widespread adoption.

Source: https://gbhackers.com/hackers-exploit-ollama-model-uploads-to-leak-server-data/

Ollama TPRM report: https://www.rankiteo.com/company/ollama

"id": "oll1777019301",
"linkid": "ollama",
"type": "Vulnerability",
"date": "4/2026",
"severity": "100",
"impact": "5",
"explanation": "Attack threatening the organization's existence"

{'affected_entities': [{'industry': 'Artificial Intelligence / Machine '
                                    'Learning',
                        'name': 'Ollama',
                        'type': 'Open-source software platform'}],
 'attack_vector': 'Malicious AI model file upload (GGUF format)',
 'data_breach': {'data_exfiltration': 'Yes (via Ollama’s registry API)',
                 'file_types_exposed': 'GGUF (GPT-Generated Unified Format) '
                                       'files',
                 'sensitivity_of_data': 'High',
                 'type_of_data_compromised': 'Sensitive server data (API keys, '
                                             'private user data, proprietary '
                                             'IP)'},
 'date_publicly_disclosed': '2026-04',
 'description': 'Cybersecurity researchers have identified a severe, unpatched '
                'vulnerability in Ollama, a widely used open-source platform '
                'for running large language models (LLMs) locally. Tracked as '
                'CVE-2026-5757, the flaw resides in Ollama’s model '
                'quantization engine and allows unauthenticated attackers to '
                'steal sensitive server data by uploading a maliciously '
                'crafted AI model file.',
 'impact': {'data_compromised': 'API keys, private user data, proprietary '
                                'intellectual property',
            'operational_impact': 'Potential full server compromise, lateral '
                                  'movement within networks',
            'systems_affected': 'Ollama servers running unpatched versions'},
 'investigation_status': 'Unpatched (as of late April 2026)',
 'lessons_learned': 'Growing risks of supply chain attacks in AI '
                    'infrastructure, particularly in open-source tools with '
                    'widespread adoption. Importance of verifying AI model '
                    'sources and enforcing strict network controls.',
 'post_incident_analysis': {'root_causes': ['Unchecked file metadata in '
                                            'quantization engine',
                                            'Unsafe memory operations in '
                                            'Go-based memory slice',
                                            'Data exfiltration via API']},
 'recommendations': ['Disable or restrict model upload functionality on '
                     'exposed servers',
                     'Limit Ollama deployments to isolated or trusted networks',
                     'Only use AI models from verified sources',
                     'Enforce strict network controls to block unauthorized '
                     'data exfiltration'],
 'references': [{'source': 'Security researcher Jeremy Brown'},
                {'source': 'CERT Coordination Center'}],
 'response': {'containment_measures': 'Disable or restrict model upload '
                                      'functionality, limit deployments to '
                                      'isolated/trusted networks, enforce '
                                      'strict network controls'},
 'title': 'Critical Unpatched Vulnerability in Ollama Exposes Sensitive Data '
          'to Attackers',
 'type': 'Vulnerability Exploitation',
 'vulnerability_exploited': 'CVE-2026-5757 (Out-of-bounds memory vulnerability '
                            'in model quantization engine)'}