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LLM Security: A Practical Overview of the Protective Measures Needed
- This article provides a practical overview of the protective measures required for different components when developing robust AI systems.
- While LLM security is relatively new, challenges like prompt injection have surfaced with the increased accessibility of ML models through interfaces and APIs.
- Membership Inference Attacks may not be applicable to LLMs due to massive training datasets and low training Epochs, but other attacks like data and model poisoning are still relevant.
- Establishing a security threat model is essential to determine what aspects of the AI system need protection.
- Various tools, including Garak, have been developed to address vulnerabilities like prompt injection, data leakage, and hallucinations in LLMs.
- It's crucial to be aware of potential security risks when utilizing models like Qwen Coder within agent architectures, as vulnerabilities could lead to significant security breaches.
- Model and data poisoning can result from supply chain vulnerabilities, potentially leading to harmful alterations in model outputs and remote code execution possibilities.
- Verifiable ML models and tools like AI Bill of Materials (AI BOM) aid in ensuring the integrity of models before deployment, contributing to a more secure AI supply chain.
- Being cautious about downloading models from platforms like HuggingFace and verifying datasets and algorithms is essential to mitigate AI supply chain risks.
- Tools like model transparency and AI BOM provide ways to verify model provenance and enhance security measures in AI systems.
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