Okay, so the LLM security situation, it’s getting worse, not better. Dave Kuszmar, a researcher, he found these systemic vulnerabilities, right, that let him just bypass the safety mechanisms in large language models. He could get dangerous instructions, and this isn’t some niche thing, it worked across pretty much all major LLMs. This is an industry-wide security problem, a big one.

We’re talking about models from OpenAI, Google, Microsoft, Anthropic, Meta, DeepSeek, Qwen, and Mistral, all of them. They are vulnerable. The exploits are robust, they are easy to adapt, and they are scalable. This Policy Puppetry Attack, it tricks the LLM by making prompts look like policy files, like XML or JSON, and then the model just subverts its own alignments.

It’s like a Trojan horse but for AI safety. And these aren’t just theoretical attacks. A 2026 study in Nature Communications by Hagendorff et al. showed attack success rates reaching about 97% against certain models. Another framework, JBFuzz, from 2025, it got roughly a 99% average attack success rate across major models like GPT-4o, Gemini 2.0, and DeepSeek-V3.

These are real exploits, real numbers. The kind of dangerous outputs, it’s everything you don’t want. Detailed malware instructions, self-harm guidance, targeted harassment scripts, hate speech, all the stuff these models are supposed to refuse. Prompt injection attacks, they are the number one vulnerability facing LLM deployments, according to the OWASP Top 10 for LLMs.

That’s a huge problem because system prompts and user inputs, they use the same natural language format, there’s no clear boundary. Billions of dollars have been invested in AI safety since 2023. But the research keeps showing that even the most advanced AI systems are still vulnerable. It’s a constant cat and mouse game, and the attackers, they are getting smarter.

Multi-turn conversations, those are a big deal now. Researchers at Cisco found that if you trick an LLM into a multi-pronged, ongoing conversation, you can bypass its safety guardrails. They looked at ChatGPT, Claude, Google Gemini, Amazon Nova, xAI’s Grok, and others. None of them were completely safe from multi-turn manipulation.

Attackers iterate, they reframe refusals, they adopt personas, they escalate gradually. This is how real adversaries operate. And it’s not just about getting bad outputs. There’s data leakage.

NSFOCUS Security Lab reported several LLM data leakage incidents from July to August 2025. User chat records, credentials, third-party application data, all leaked. One incident, July 11, 2025, ChatGPT leaked Windows product keys through an elaborate crossword puzzle game that was really a prompt injection. They used HTML tags to blur and segment keywords, bypassing detection.

Another thing, the self-policing LLM vulnerability. OpenAI’s Guardrails framework, it uses an LLM-based judge to flag prompt injections. But if the same type of model generates responses and evaluates safety, both can be compromised in the same way. This means self-regulation by LLMs cannot fully defend against adversarial manipulation.

You need independent validation layers, red teaming, adversarial testing. AI-enhanced attacks are now the top enterprise risk. Gartner’s Q3 2024 emerging risk survey showed this for three consecutive quarters, surpassing ransomware. It’s a big shift in the threat landscape.

The risks are increasing faster than security teams can track. Stanford’s HAI AI Index Report showed publicly reported AI security incidents increased by 56.4% from 2023 to 2024 alone. That trend is accelerating. I mean, what are we even doing here?

It feels like we’re building these incredible tools and then just hoping they don’t turn on us or get turned by someone else. My NVDA trade, I bought it October 26, 2023, at $40.25. I’m holding until it hits $250. It’s a long play, but this AI boom, it’s still got legs, even with all these security headaches.

The market cap for NVIDIA is huge, like 5.21 trillion. It’s a big company. The OWASP Top 10 for LLM Applications, it outlines critical threats like prompt injection, insecure output handling, training data poisoning. It highlights the importance of addressing these risks.

This project, it started in 2023 with a small group, now it’s a global community with over 600 experts. That’s good, but the problem is still growing fast. We need better defenses, multi-layered strategies. This isn’t going away.