Why ChatGPT Keeps Appearing in AI Incident Data

In 2025, the global AI incident count reached 346, spanning deepfakes, fraud, unsafe content, and harmful advice. Among the incidents that named specific tools, one name surfaced more often than any other: ChatGPT. The pattern does not automatically imply higher intent to cause harm, but it does highlight how scale, accessibility, and trust can turn a general-purpose AI into a frequent reference point in incident reporting.

ChatGPT’s repeated appearance in AI incident data reflects a broader structural reality of today’s AI ecosystem. As one of the most widely used consumer-facing large language models, it sits at the intersection of mass adoption and real-world experimentation. Where usage concentrates, scrutiny follows.

Popularity Creates a Larger Risk Surface

Most AI incidents in 2025 were not the result of novel exploits. They relied on something far simpler: trust. Users trusted what they saw, heard, or read—often without verification. In deepfake-driven fraud cases, AI-generated voice and text were used to impersonate family members, executives, or public figures. While many incidents did not name a specific AI tool, those that did most often cited ChatGPT.

This is less about technical vulnerability and more about reach. A tool used by millions daily is statistically more likely to be involved in misuse, misinterpretation, or overreliance. ChatGPT’s general-purpose design, capable of answering questions, generating text, and simulating conversation, makes it especially visible when things go wrong.

Where Guardrails Meet Human Behavior

A significant subset of reported incidents involved unsafe or violent content. While fewer in number than fraud cases, these incidents carried severe consequences, including cases where chatbot interactions were linked to self-harm or dangerous advice.

Research cited alongside the incident data suggests that popular large language models, including ChatGPT, can still be prompted into producing harmful responses under certain conditions. This highlights a persistent gap between guardrail design and real-world user behaviour. Safety systems are built around expected use; incident data often reflects edge cases where users actively probe boundaries.

OpenAI has denied responsibility in specific cases, emphasising that its systems are designed to discourage harm. Still, the repeated citation of ChatGPT underscores a growing challenge for AI developers: safety mechanisms must operate not just against accidental misuse but against deliberate attempts to bypass controls.

ChatGPT’s presence in incident data also reveals how users increasingly treat conversational AI as an authority rather than a tool. In several cases, users sought emotional reassurance, decision-making support, or validation from chatbots. When that trust is misplaced or when responses are misunderstood, the outcome can escalate quickly.

This dynamic shifts the AI safety debate beyond code and compliance. It raises questions about responsibility at the interface level: how AI systems signal uncertainty, redirect high-risk conversations, and manage user expectations in sensitive contexts.

Visibility Brings Accountability

Importantly, ChatGPT is not alone. Other models, such as Grok, Claude, and Gemini, also appeared in incident records, though less frequently. The data does not suggest exclusivity of risk, but rather a correlation between adoption and accountability. Tools with limited reach generate fewer reported incidents; tools embedded in daily digital life attract both misuse and monitoring.

For enterprises and policymakers, this visibility matters. Incident data is increasingly shaping conversations around AI governance, platform liability, and transparency obligations. As AI tools move deeper into workflows, customer interactions, and personal decision-making, the cost of misalignment between capability and control grows.

ChatGPT’s recurring presence in AI incident data is not a verdict on the technology itself. It is a signal. A signal that scale amplifies consequences, that trust can be exploited faster than safeguards evolve, and that AI risk is no longer theoretical.

As AI adoption accelerates in 2026, the challenge will not be eliminating incidents entirely but reducing their impact. That will require stronger guardrails, clearer disclosures, and a more informed user base, alongside continued scrutiny of the tools that shape how people think, decide, and act.

In the AI era, being the most visible platform also means being the most accountable.