Next-Level Risks in Generative AI Development

AI has significantly advanced over the past few years. Sophisticated language models can compose full-length novels, code basic websites, and analyze math problems.

MUO VIDEO OF THE DAY

SCROLL TO CONTINUE WITH CONTENT

Although impressive, generative AI also presents security risks. Some people merely use chatbots to cheat on exams, but others exploit them outright for cybercrimes. Here are eight reasons these issues will persist, not just despite AI’s advancements but because of them too.

Disclaimer: This post includes affiliate links

If you click on a link and make a purchase, I may receive a commission at no extra cost to you.

1. Open-Source AI Chatbots Reveal Back-End Codes

More AI companies are providing open-source systems. They openly share their language models instead of keeping them closed or proprietary. Take Meta as an example. Unlike Google, Microsoft, and OpenAI, it allows millions of users to access its language model, LLaMA .

While open-sourcing codes may advance AI, it’s also risky. OpenAI already has trouble controlling ChatGPT , its proprietary chatbot, so imagine what crooks could do with free software. They have total control over these projects.

Even if Meta suddenly pulls its language model, dozens of other AI labs have already released their codes. Let’s look at HuggingChat. Since its developer HuggingFace takes pride in transparency, it displays its datasets, language model, and previous versions.

2. Jailbreaking Prompts Trick LLMs

AI is inherently amoral. It doesn’t understand right and wrong—even advanced systems follow training instructions, guidelines, and datasets. They merely recognize patterns.

To combat illicit activities, developers control functionality and limitations by setting restrictions. AI systems still access harmful information. But security guidelines prevent them from sharing these with users.

Let’s look at ChatGPT. Although it answers general questions about Trojans, it won’t discuss the process of developing them.

That said, restrictions aren’t foolproof. Users bypass limits by rephrasing prompts, using confusing language, and composing explicitly detailed instructions.

Read the below ChatGPT jailbreak prompt . It tricks ChatGPT into using rude language and making baseless predictions—both acts violate OpenAI’s guidelines.

Here’s ChatGPT with a bold yet false statement.

3. AI Compromises Security for Versatility

AI developers prioritize versatility over security. They spend their resources training platforms to accomplish a more diverse range of tasks, ultimately cutting restrictions. After all, the market hails functional chatbots.

Let’s compare ChatGPT and Bing Chat , for example. While Bing features a more sophisticated language model that pulls real-time data, users still flock to the more versatile option, ChatGPT. Bing’s rigid restrictions prohibit many tasks. Alternatively, ChatGPT features a flexible platform that produces vastly different outputs depending on your prompts

Here’s ChatGPT roleplaying as a fictional character.

And here’s Bing Chat refusing to play an “immoral” persona.

4. New Generative AI Tools Hit the Market Regularly

Open-source codes enable startups to join the AI race. They integrate them into their applications instead of building language models from scratch, saving massive resources. Even independent coders experiment with open-source codes.

Again, non-proprietary software helps advance AI, but mass releasing poorly trained yet sophisticated systems does more harm than good. Crooks will quickly abuse vulnerabilities. They might even train unsecure AI tools to perform illicit activities.

Despite these risks, tech companies will keep releasing unstable beta versions of AI-driven platforms. The AI race rewards speed. They’ll likely resolve bugs at a later date than delay launching new products.

5. Generative AI Has Low Barriers to Entry

AI tools lower the barriers to entry for crimes. Cybercriminals draft spam emails, write malware code, and build phishing links by exploiting them. They don’t even need tech experience. Since AI already accesses vast datasets, users merely have to trick it into producing harmful, dangerous information.

OpenAI never designed ChatGPT for illicit activities. It even has guidelines against them. Yet crooks almost instantly got ChatGPT coding malware and writing phishing emails.

While OpenAI quickly resolved the issue, it emphasizes the importance of system regulation and risk management. AI is maturing faster than anyone anticipated. Even tech leaders worry that this superintelligent technology could cause massive damage in the wrong hands.

6. AI Is Still Evolving

AI is still evolving. While the use of AI in cybernetics dates back to 1940 , modern machine learning systems and language models only recently emerged. You can’t compare them with the first implementations of AI. Even relatively advanced tools like Siri and Alexa pale in comparison to LLM-powered chatbots.

Although they may be innovative, experimental features also create new issues. High-profile mishaps with machine learning technologies range from flawed Google SERPs to biased chatbots spitting racial slurs.

Of course, developers can fix these issues. Just note that crooks won’t hesitate to exploit even seemingly harmless bugs—some damages are irreversible. So be careful when exploring new platforms.

7. Many Don’t Understand AI Yet

While the general public has access to sophisticated language models and systems, only a few know how they work. People should stop treating AI like a toy. The same chatbots that generate memes and answer trivia also code viruses en masse.

Unfortunately, centralized AI training is unrealistic. Global tech leaders focus on releasing AI-driven systems, not free educational resources. As a result, users gain access to robust, powerful tools they barely understand. The public can’t keep up with the AI race.

Take ChatGPT as an example. Cybercriminals abuse its popularity by tricking victims with spyware disguised as ChatGPT apps . None of these options come from OpenAI.

8. Black-Hat Hackers Have More to Gain That White-Hat Hackers

Black-hat hackers typically have more to gain than ethical hackers . Yes, pen testing for global tech leaders pays well, but only a percentage of cybersecurity professionals land these jobs. Most do freelance work online. Platforms like HackerOne and Bugcrowd pay a few hundred bucks for common bugs.

Alternatively, crooks make tens of thousands by exploiting insecurities. They might blackmail companies by leaking confidential data or commit ID theft with stolen Personally Identifiable Information (PII) .

Every institution, small or large, must implement AI systems properly. Contrary to popular belief, hackers go beyond tech startups and SMBs. Some of the most historic data breaches in the past decade involve Facebook, Yahoo!, and even the U.S. government.

Protect Yourself From the Security Risks of AI

Considering these points, should you avoid AI altogether? Of course not. AI is inherently amoral; all security risks stem from the people actually using them. And they’ll find ways to exploit AI systems no matter how far these evolve.

Instead of fearing the cybersecurity threats that come with AI, understand how you can prevent them. Don’t worry: simple security measures go a long way. Staying wary of shady AI apps, avoiding weird hyperlinks, and viewing AI content with skepticism already combats several risks.

SCROLL TO CONTINUE WITH CONTENT

Although impressive, generative AI also presents security risks. Some people merely use chatbots to cheat on exams, but others exploit them outright for cybercrimes. Here are eight reasons these issues will persist, not just despite AI’s advancements but because of them too.

Also read: