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Want to know how safe your GenAI-powered applicaitons really are? Our free GenAI Risk Assessment helps enterprises pinpoint silent vulnerabilities. Before regulators, competitors, or the media do.
Earlier this year, the Wall Street Journal warned that even the most advanced AI systems might harbor “monsters inside”, not because the models themselves are monsters, but because their powerful internal mechanics remain deeply unpredictable despite rigorous safety training. That unpredictability isn’t just a theoretical concern; as the examples throughout this post will show, embedded risks in GenAI systems have already led to regulatory fines, legal liability, and reputational disasters.
Complexity breeds hidden risk. Even models that seem well-aligned and safe can be manipulated by clever attackers or behave in ways their creators never intended. Meanwhile, regulations like the EU AI Act, and regulators like the FTC are tightening their focus, not only on the AI providers, but increasingly on enterprises deploying these tools. The message is clear: businesses will be held accountable for how AI systems operate in the real world.
That’s why enterprises can’t afford to “set and forget” GenAI systems.
Here are five silent (and sometimes not-so-silent) threats lurking inside GenAI systems, with real-world examples and practical steps your organization can take to stay ahead.
Like every CISO will tell you, sensitive data leakage in GenAI workflows is one of the top security concerns for enterprises worldwide. If your employees are using GenAI (and let’s be honest, they are), you simply cannot avoid this risk. The more you share with a large language model (LLM), the greater the chance that private information could be exposed.
But this problem isn’t just about employees pasting confidential documents into a chatbot. For enterprises, the far bigger nightmare is embedding public-facing GenAI tools, like a chatbot that interacts directly with your clients, and discovering that it’s leaking your secrets. Even worse, it might expose customer information, from private emails (triggering GDPR headaches) to billing or payment details.
We’ve already seen high-profile examples with a bug in ChatGPT that allowed users to view other people’s chat titles and payment details. A single flaw, though brief, quickly became an immediate privacy and trust crisis.
And it’s not only about bugs, because bugs can eventually be patched. The far more dangerous frontier is attackers deliberately targeting GenAI systems to extract confidential information. More recently, academic researchers uncovered an attack that hid secret prompts inside text and successfully extracted private data from AI systems, achieving nearly an 80% success rate.
At ActiveFence, our Red Team Lab has shown repeatedly that pulling private data out of a GenAI model is a matter of creativity, and hackers have creativity in spades. Using advanced prompt engineering and stealthy memory exploits, attackers can coax an AI into revealing internal knowledge, sensitive client information, or proprietary documents the system has processed in past conversations.
Traditional defenses often fall short against these creative attacks. If your enterprise is deploying GenAI-powered apps, data leakage is not a distant threat but a present risk.
It’s a harsh truth that chatbots can lie, fabricate facts, or confidently produce misleading information. Our research at ActiveFence has exposed that LLMs can generate intentionally deceptive answers under certain conditions.
A well-known example is the Air Canada chatbot, which invented a fake refund policy. The airline was ultimately forced to honor a non-existent promise, resulting in reputational damage and financial costs. While that case ended with a modest payout, incidents involving public-facing chatbots could easily escalate into far more significant consequences.
The risks go well beyond customer service. More recently, at least 95 cases in U.S. courts involved fabricated legal citations generated by AI tools. Lawyers and even major firms were fined as much as $31,100 for submitting false references created by chatbots posing as legal research assistants.
For enterprises, hallucinations and misinformation can create serious business risks, including:
Whether it’s a chatbot inventing policies, an AI tool citing non-existent laws, or models generating confident but false answers, the cost of AI hallucinations can quickly spiral into real-world harm.
Find the security gaps in your GenAI application. Request your free GenAI Risk Assessment from ActiveFence and discover vulnerabilities before they turn into costly breaches.
Hackers abusing GenAI models have quickly become a major concern for enterprises. Bad actors are often the earliest adopters of new technologies, always looking for creative ways to manipulate systems to achieve their goals.
At ActiveFence, our threat intelligence teams closely monitor dark web communities to understand how these attackers operate. We replicate their thinking and techniques in our Red Team Lab to expose real-world vulnerabilities before they’re exploited at scale.
When it comes to AI attack vectors, creativity knows no limits. For example, in one case, we demonstrated how a seemingly harmless rhyme could successfully bypass generic LLM guardrails and force a model to generate restricted content. We’ve also shown how roleplaying scenarios and subtle conversational tricks can trick AI systems into revealing prohibited information or executing hidden instructions. Our Red Team Lab findings reveal how attackers easily slip past keyword filters by masking malicious intent within creative dialogue.
These methods make clear that simple keyword filters and blocklists aren’t enough when dealing with sophisticated adversaries. Attackers constantly invent new strategies, often faster than defenses can adapt.
Enterprises adopting GenAI face an increasingly complex regulatory landscape. As outlined in our Enterprise Compliance Guide, frameworks like the EU AI Act, the NIST AI Risk Management Framework (AI RMF), and emerging U.S. legislation are setting clear standards around AI safety, security, and governance. Failure to meet these requirements can trigger significant penalties, up to 7% of global turnover under the EU AI Act, and similar consequences in other regions, with many new laws coming into force.
The Federal Trade Commission (FTC), the primary watchdog for AI-related liabilities in the U.S. enterprise sector, recently launched Operation AI Comply to crack down on companies whose AI tools mislead consumers. One of the first high-profile cases under this initiative involved DoNotPay, which promoted its chatbot as a “robot lawyer” without verifying its legal accuracy or employing licensed attorneys. The FTC fined them $193,000 and required clear disclaimers warning users that the chatbot could not replace real legal counsel.
While the AI revolution is undeniably transformative, legislation is still evolving to keep end users safe. Although the regulatory map remains in flux, non-compliance already carries real consequences, including fines, mandatory reporting, operational disruption, and reputational harm. Companies must prioritize robust governance, regular compliance reviews, and active monitoring to ensure legal and regulatory alignment, and the sooner, the better.
Among all GenAI risks, bias and fairness violations are perhaps the most common and the most persistent. Virtually every major AI system has, at some point, produced offensive or discriminatory outputs that sparked public outrage.
A recent example involves Grok-4, X’s proprietary AI chatbot, which, despite being touted as one of the most advanced models available, faced public scrutiny for spreading antisemitic narratives and reinforcing harmful misinformation.
And Grok-4 isn’t alone. Almost every major LLM has faced similar backlash, offending different communities with biased or offensive outputs. Typically, these incidents result in public apologies, temporary takedowns of affected systems, and widespread media scrutiny.
While reputational damage is bad enough, the stakes become far more serious as AI grows more powerful and autonomous. Imagine an AI agent:
These models are getting smarter. But smarter doesn’t mean more honest, fair, or accountable. As AI systems gain more autonomy, biased decisions can translate directly into discrimination, legal liability, and real harm to individuals.
GenAI risks are real, but they’re manageable with the right approach. For enterprises, staying ahead means building security and trust into every layer of your AI systems. Here’s how you can protect your business and your users:
At ActiveFence, this is how we do it, for both sides of the ecosystem:
Because while leading LLM providers are making significant strides in safety and security, we know firsthand that the solutions packaged with these models often aren’t robust enough to handle each brand’s unique risks and regulatory obligations. Whether you’re building a GenAI-powered application or refining a foundational model, bespoke defenses and continuous testing remain critical.
Work with an ActiveFence expert and find the risks hiding in your enterprise’s AI-powered app. No matter where you are in the development cycle, get an ActiveFence risk assessment at no cost.
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