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Sadly, this risk now includesopen sourceartificial intelligence (AI) software.

Hugging Face does not stand alone in its vulnerability.

Concept art representing cybersecurity principles

Nytt DDoS-rekord

Chief Product Officer at Exabeam.

CISOs and security professionals should be proactive about detecting malicious datasets and responding quickly to potential supply chain attacks.

To do that, you must be aware of what these threats look like.

For example, a Prompt Injection vulnerability allows adversaries to manipulate an LLM through cleverly crafted inputs.

This can also create severe reputational or legal risks if these vulnerabilities are discovered after deployment.

Further vulnerabilities involve insecure output handling and the challenges in differentiating intended versus dangerous input to an LLM.

Without proper filtering and output validation, malicious actors could compromise entire stages of the software development lifecycle.

This approach can involve using tools like theOpenAIModeration API to ensure safer filtering.

To enhance the security of LLMs, developers can leverage cryptographic techniques such as digital signatures.

This process ensures the model’s authenticity and integrity, preventing unauthorized modifications and tampering.

Watermarking is another effective technique for safeguarding LLMs.

Even if the model is duplicated or stolen, the watermark remains embedded, allowing for detection and identification.

SBOMs are critical for understanding the software’s composition, especially for tracking vulnerabilities, licenses, and dependencies.

Note that AI-specific versions are currently in development.

For ML applications, this advancement is profound.

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The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.

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