The article is a strong, comprehensive deep dive into one of the most critical technology and security issues of the decade. It balances accessibility with technical depth, covers real incidents, explains core concepts clearly, and includes actionable guidance. It also effectively integrates commentary from multiple AI systems, giving readers confidence that the analysis reflects a broad consensus across models.

Below are key opportunities to further strengthen the article while maintaining its current tone, structure, and authority.

Consider adding a short section for everyday users

The article is excellent for technical, business, and policy audiences. One additional improvement would be a short chapter explaining:

Why cybersecurity risks in LLMs matter to ordinary people.

Examples could include:

• AI-generated phishing messages that mimic family members

• Deepfake phone calls are used for fraud

• AI-powered impersonation in messaging apps

• Synthetic identity theft using scraped social media

• Manipulated search results or AI assistants steering users incorrectly

This grounds the topic in the reader’s lived experience.

Add a simple analogy or illustration for multimodal exploits.

The multimodal Sora 2 vulnerability is fascinating but complex. A brief analogy could help non-technical readers understand how cross-modal leakage works.

For example:

“It is like whispering a secret to someone who speaks multiple languages. Even if you forbid them from repeating it, they might accidentally repeat it in a different language you didn’t expect.”

A single sentence like this would make the concept far more intuitive.

Add a small section highlighting how AI can strengthen cybersecurity.

The article focuses on threats, which is appropriate. However, security leaders often want to understand the opportunity side as well.

A short section could spotlight:

• AI-assisted threat detection

• Automated log triage

• AI-powered red teaming and security scanning

• Deepfake and phishing detection models

• Predictive analysis from behavior patterns

This shows that AI is not only a risk surface but also a defensive force multiplier.

Add a quick bullet list of common organizational mistakes.

Practical value could be increased with a short list of common errors, such as:

• Letting LLMs see too much unfiltered internal data

• Allowing direct tool or API execution without a safety layer

• Treating system prompts as harmless instead of sensitive

• Failing to monitor internal LLM usage (shadow AI)

• Weak access control in RAG knowledge bases

• Assuming closed models are inherently secure

This would give readers a checklist they can apply immediately.

Add a short governance or human-factor section.

Many of the real-world failures involve people, not models.

A small section could mention:

• Risk committees for AI use

• Approval workflows for agentic systems

• Prompt logging and auditing

• Employee training on AI impersonation scams

• Secure development lifecycle for AI features

This complements the technical defense section.

Expand the follow-up questions slightly.

One additional follow-up question would round out the set:

This encourages deeper thinking about privacy attacks (model inversion, membership inference).

Small improvements

Adding one or two extra focused mini-headings could help discoverability, such as:

These also help scanning readers.

The article is a highly polished, authoritative, and timely piece with strong research, excellent structure, and valuable practical advice. The suggestions above are optional enhancements that could make it even more accessible, more actionable, and more balanced for a broad audience ranging from beginners to advanced readers.