A large language model (LLM) is a deep learning AI model or system that understands, generates, and predicts text-based content, often associated with generative AI.

In the current technological landscape, we have robust and known models like:-

• ChatGPT
• Google Bard 
• Meta’s LLaMA

Cybersecurity analysts at the National Cyber Security Centre (NCSC) have recently unveiled and warned of specific vulnerabilities in AI systems or models like ChatGPT or Google Bard, Meta’s LLaMA.

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Vulnerabilities

While LLMs have a role, don’t forget cybersecurity basics for ML projects. Here below, we have mentioned the specific vulnerabilities in AI models about which the researchers at NCSC warned:-

• Prompt injection attacks: A major issue with current LLMs is ‘prompt injection,’ where users manipulate inputs to make the model misbehave, risking harm or leaks. Multiple prompt injection cases exist, from playful pranks like Bing’s existential crisis to potentially harmful exploits like accessing an API key through MathGPT. The prompt injection risks have risen since the LLMs feed data to third-party apps. 

• Data poisoning attacks: LLMs, like all ML models, rely on their training data, which often contains offensive or inaccurate content from the vast open internet. The NCSC’s security principles highlight ‘data poisoning,’ and research by Nicholas Carlini shows poisoning large models with minimal data access is possible.

Prevention mechanisms

Detecting and countering prompt injection and data poisoning is tough. System-wide security design, like layering rules over the ML model, can mitigate risks and prevent destructive failures.

Extend cybersecurity basics to address ML-specific risks, including:-

• Supply chain security
• User education
• Access controls
• NCSC’s ML security principles

Cyber secure principles

Beyond LLMs, recent months revealed ML system vulnerabilities due to insufficient cybersecurity principles, such as:-

• Think before you arbitrarily execute code you’ve downloaded from the internet (models)
• Keep up to date with published vulnerabilities and upgrade software regularly.
• Understand software package dependencies.
• Think before you arbitrarily execute code you’ve downloaded from the internet (packages)

However, in a rapidly evolving AI landscape, maintaining strong cybersecurity practices is essentially important, regardless of ML presence.

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