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ai security
Threat Actors Actively Targeting LLMs
Security researchers operating Ollama honeypots detected 91,403 attack sessions over a four-month period and identified two distinct threat campaigns systematically targeting large language model (LLM) deployments. The attacks demonstrate that threat actors are actively mapping and probing the growing attack surface created by widespread AI infrastructure.
Why it matters: Organizations deploying LLMs and other AI models need to implement monitoring and access controls immediately, as adversaries are actively scanning for exposed instances to compromise data, compute resources, or inject malicious responses.
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Correction
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ai security
Threat Actors Actively Targeting LLMs
No summary had been written when this copy was frozen.
- First seen by Cybersecurity Tracker
Source attribution
Correction
Correction recorded as of .
ai security
Threat Actors Actively Targeting LLMs
Honeypot infrastructure monitoring Ollama instances logged over 91,000 attack sessions from October 2025 through January 2026. Analysis identified two distinct threat campaigns actively targeting large language model deployments to probe for vulnerabilities.
Why it matters: Organizations running Ollama or other LLM services face systematic reconnaissance by threat actors; practitioners should review exposed LLM infrastructure and limit access to trusted networks.
- Source published
- First seen by Cybersecurity Tracker