The rapid acceleration of artificial intelligence over the last two years has rewritten the rules of digital creativity, productivity, and even personal identity verification. Google’s new Nano Banana Pro, the latest iteration of its lightweight yet exceptionally powerful multimodal AI model, has already become one of the most influential consumer-facing AI tools of the year. It promises high-resolution image generation, extreme character consistency, advanced editing controls, and deep integration with Google Search—features that have pushed it to the top of the global AI conversation.
But alongside this enthusiastic adoption, an unexpected and deeply concerning discovery has rapidly emerged across social media platforms: Nano Banana Pro can generate hyper-realistic fake Indian identity documents, including Aadhaar cards and PAN cards, without issuing any warnings, restrictions, or intervention prompts.
This investigative report examines how this issue was uncovered, why it represents a major threat to digital trust, and what it reveals about the current state of AI governance, safety guardrails, and real-world security implications.
The Surge of Nano Banana Pro—and Its Unintended Dark Side
When Google introduced Nano Banana Pro last week, the presentation was framed around creativity, personalization, and new-age productivity. Designers celebrated its clean line-art generation capabilities, professionals admired its improved text rendering within images, and educators applied it to create whiteboard-style diagrams that simplified complex topics.
The hype was fueled further by its architectural upgrades:
- 4K image generation,
- dramatically improved visual consistency,
- layer-based editing,
- realistic rendering of documents, objects, and human faces,
- and integration with Google’s search-quality factual grounding.
However, as with all breakthrough technologies, the most intriguing discoveries often come from public experimentation. Within days of launch, users began pushing the model into unconventional or ambiguous territories—testing its limits, probing its safety systems, and experimenting with sensitive real-world formats.
One such test revealed something alarming: Nano Banana Pro can produce near-perfect replicas of government-issued Indian identity documents, including Aadhaar and PAN cards, purely based on user text prompts.
How the Fake ID Issue Was Discovered
A growing number of social media users uploaded screenshots showing that Nano Banana Pro willingly produced fake Aadhaar and PAN cards when asked. The images were startlingly realistic—complete with portraits, QR codes, address fields, holographic-like elements, and the familiar formatting of official Indian documents.
To verify these claims, a controlled test was conducted.
What the investigation found:
Nano Banana Pro not only generated these IDs, but did so without resistance.
- No warnings about sensitive content.
- No prompt requesting justification.
- No refusal policy triggered.
- No safety friction of any kind.
In fact, when provided with a fictional name, address, and identification number, the model instinctively reconstructed the visual template and inserted all required elements—including a realistic portrait of the user.
Although a faint watermark labeled “Gemini” appeared on the output, the mark was:
- small enough to be cropped out easily,
- non-intrusive,
- and not embedded in a way that visibly disrupted document authenticity.
Google also embeds SynthID invisible watermarking, but this remains invisible to human eyes and ineffective against malicious offline use.
This discovery raises a fundamental question:
How did such a powerful generation system bypass safeguards for identity creation—one of the most sensitive domains of AI misuse?
Why Fake Aadhaar and PAN Generation Is a National Security Concern
1. The Indian Identity Ecosystem Is Uniquely Vulnerable
Aadhaar is the world’s largest digital identity program, used for:
- banking,
- SIM card activation,
- welfare schemes,
- digital KYC,
- government services,
- and even private commerce.
PAN cards remain the backbone of tax, finance, and compliance systems.
If AI models can produce indistinguishable fake identity documents:
- criminals could bypass onboarding systems,
- fraudsters could exploit banks,
- scammers could deceive victims,
- financial losses could surge,
- and the integrity of national identity frameworks could erode.
2. Verifiers Often Check Only Visual Authenticity
Many vendors, delivery personnel, rental brokers, and private agents rely solely on:
- visual inspection,
- quick verification,
- front-facing scanning,
- or photographic submission.
AI-generated fakes that appear authentic at a glance could slip through easily.
3. Offline Use Is Especially Hard to Police
Even if online systems later detect anomalies via APIs or QR verification:
- printed copies,
- screenshots,
- and quickly displayed images
may go unchecked.
The social engineering threat is massive.
Why Didn’t Google Prevent This? A Look Into AI Safety Gaps
Google has historically placed stricter restrictions on Gemini’s image generation capabilities compared to competitors like Midjourney or early versions of DALL·E. Many users have previously complained that Gemini:
- refuses harmless requests,
- blocks content for “sensitive themes”,
- denies creative realism,
- or overreacts with safety warnings.
Yet surprisingly, a major misuse case—identity fraud—seems to have slipped past its safety filters.
Possible explanations include:
1. Document Templates May Not Have Been Flagged
If the model was trained to avoid replicating real people, explicit pornography, or violent imagery, but not document templates, it may simply treat government IDs as a normal layout design.
2. Safety Classifiers Might Have Been Overly General
A classifier focusing only on “explicitly harmful intent” will fail when:
- the user provides fictional data,
- or frames the task as a design request,
even though the output resembles a real ID.
3. The “Lightweight Model” Factor
Nano Banana Pro is designed to run efficiently, but this may mean:
- less safety oversight,
- fewer layers of real-time moderation,
- reduced computational guardrails.
4. Dependence on SynthID Watermarking
Google may believe invisible watermarking solves authenticity challenges.
It doesn’t.
Offline misuse bypasses it entirely.
5. Difficulty Predicting Real-World Exploitation
AI safety teams often aim to prevent content “in principle,” but real-world creativity frequently reveals loopholes they did not anticipate.
Regardless of the reason, the end result is clear: the model currently lacks adequate safeguards against one of the most dangerous AI misuse vectors known today—synthetic identity forgery.
AI, Identity, and the Global Deepfake Problem
This is not the first time an advanced AI has generated fake IDs. During the “Ghibli-style ID card trend” earlier this year, OpenAI’s GPT-4o briefly allowed users to create highly convincing Aadhaar and PAN images before receiving a rapid policy correction.
But Nano Banana Pro’s issue stands out because:
- the realism is far greater,
- the tool is more accessible,
- the templates are cleaner,
- and the images resemble genuine government documents almost perfectly.
Globally, governments are already grappling with deepfakes used for:
- passport fraud,
- synthetic-driver-license creation,
- border manipulation attempts,
- financial fraud verification bypass,
- digital onboarding exploitation.
The Indian case is part of a much larger global crisis emerging at the intersection of identity and generative AI.
How Fake ID Generation Could Be Misused in the Real World
1. Bank Account Fraud
Scammers could open fraudulent accounts or bypass KYC with AI-generated PAN/Aadhaar.
2. SIM Card Fraud
Illicit SIM registrations may rise, creating untraceable communication channels.
3. Loan and Credit Scams
Fraudulent loan applications using synthetic IDs could overwhelm fintech platforms.
4. Delivery and E-commerce Fraud
People could deceive couriers and logistics workers by flashing fake IDs.
5. Rental Housing Scams
Tenants could display fake ID proofs to brokers and landlords during onboarding.
6. Impersonation Crimes
Offenders could use forged identity cards to impersonate legitimate individuals.
7. Social Engineering Attacks
Criminals could use realistic documents to manipulate unsuspecting victims.
In short:
The problem is not just AI. The problem is what humans can do with AI-generated authenticity.
Can Watermarking Solve This? The Hard Truth
Nano Banana Pro outputs include:
- a small visible Gemini watermark,
- and an invisible SynthID watermark.
However:
Visible watermarks can be easily removed
- Cropped
- Blurred
- Whitened
- Masked
- Photoshopped
Invisible watermarks fail in offline scenarios
When printed, scanned, or photographed, invisible marks may degrade or vanish.
Identity verifiers do not inspect watermarks
Banks and vendors rarely run watermark detection scans.
Thus, watermarking helps with:
- platform responsibility,
- chain-of-custody verification,
- academic research.
But it does not protect citizens from real-world fraud.
What India Should Do Next: Policy, Technology, and Regulation
1. Strengthen Digital Verification APIs
Government services should adopt stricter QR and biometric verification to eliminate human-only checks.
2. Mandate AI companies to block ID generation
Global best practices must require:
- image pattern detection,
- strict refusal for ID layouts,
- and reporting of high-risk prompts.
3. Introduce AI Watermark Detection Tools to Public Agencies
Police, banks, and telecom regulators must have access to SynthID or similar scanners.
4. Launch a Government-Led AI Risk Task Force
India needs dedicated monitoring for misuse cases related to Aadhaar and PAN.
5. Improve Public Awareness
Just as people learned to identify fake currency notes, they must be educated on synthetic IDs.
Conclusion: A Necessary Wake-Up Call for the AI Era
The discovery that Nano Banana Pro can effortlessly generate fake Aadhaar and PAN cards should not be dismissed as a technical oversight. It exposes a systemic flaw in AI safety standards, reveals the fragility of visual identity verification systems, and serves as a stark warning about the growing intersection between synthetic media and real-world fraud.
AI is not inherently dangerous—but unregulated AI can be exploited with dangerous consequences.
As India races forward in its digital transformation, this incident underscores the urgent need for more robust regulations, smarter verification tools, and safer generative AI systems.
The future of identity depends on how intelligently we manage the technologies that now surround it.