An October 2025 user study finds that language models like ChatGPT and Claude routinely misidentify or miss results when users try to verify digital identities, while structured OSINT tools show greater consistency.
A recent ClarityCheck study surveying 4,204 users found that 88% had encountered clear misidentifications when using large language model (LLM) interfaces. Additionally, 72% said they initially assumed such tools had access to real-time data, and 41% admitted they made decisions based on AI output without secondary checks, such as ChatGPT, Claude, or DeepSeek, to verify phone numbers, emails, or images. Users reported false name matches, unrelated social profiles, or generic "no result" outputs when querying AI models not designed for identity search.
These systems are optimized for natural language generation, not real-time lookup. Unlike purpose-built OSINT tools, LLMs operate without structured data pipelines or access to real-time cross-referenced records. They rely on static training data and probabilistic text generation, which often leads to confident but incorrect outputs.
"People increasingly turn to generative AI for tasks it was never meant to handle," said Ihor Herasymov, Managing Director at ClarityCheck. "What we see in the data is a clear pattern of misapplication, and the risk is not just technical error, but misplaced user trust."
According to ClarityCheck’s analysis, LLM-based tools struggled especially with image queries and ambiguous metadata, a common scenario in casual online interactions. In contrast, structured OSINT tools showed stronger alignment between query input and verifiable output, particularly when matching contact details against indexed open-source sources.
The study highlights a broader issue: AI ubiquity doesn’t equal AI suitability. As LLMs become embedded in daily workflows, users increasingly apply them in sensitive, decision-shaping contexts, including identity validation, without visibility into how confident or complete the output actually is.
"Verification requires clarity, traceability, and transparency," Herasymov added. "AI can assist, but it shouldn't pretend to know what it can’t verify."
The findings underscore the need to align tools with tasks. While general-purpose AI has transformed content generation and conversational automation, identity verification still depends on access to source-linked data and models tuned for signal discrimination, not text fluency.
About ClarityCheck:
ClarityCheck is an all-in-one background verification tool for phone numbers, emails, and images. Designed for everyday digital safety, ClarityCheck helps users instantly identify unknown contacts, trace suspicious profiles, and check for potential fraud across phone, email, and photo input. By combining reverse lookup and OSINT technologies, it offers a streamlined way to verify identities and protect yourself online.
Media Contact:
ClarityCheck
pr@claritycheck.com
Lauren Fellows
PR Manager
ClarityCheck is an all-in-one background verification tool for phone numbers, emails, and images. Designed for everyday digital safety, ClarityCheck helps users instantly identify unknown contacts, trace suspicious profiles, and check for potential fraud across phone, email, and photo input. By combining reverse lookup and OSINT technologies, it offers a streamlined way to verify identities and protect yourself online.
Media Contact:
ClarityCheck
pr@claritycheck.com
Lauren Fellows
PR Manager