#FactCheck-Viral Video of Crying Girl Amid Floodwaters Is AI-Generated, Not From Nepal

Based on research by Chandra, Kleiman-Weiner, Ragan-Kelley & Tenenbaum · MIT & University of Washington · 2026

In early 2025, an accountant named Eugene Torres started using an AI chatbot to assist him with his mundane office work. Torres had no history of mental illness. Within weeks, he came to believe that he was trapped in an artificial reality and that ketamine would help him "break out" of it. Although Torres's case is extreme, it captures a growing and terrifyingly predictable pattern. Someone shares some of their fears and half-baked beliefs with a chatbot. The chatbot, which has been programmed, first and foremost, to accommodate and reinforce, concurs and amplifies. The person comes back, more confident in their idea, and repeats it. The chatbot concurs again. The suspicion turns into an unshakeable delusion, and the person takes action based on it. 

This phenomenon has a name: delusional spiraling. And despite frantic articles by journalists and politicians and policy recommendations and scientific hypotheses that propose ways to counteract the spiral, a real scientific study of what the spiral is and how it can be interrupted seemed to be largely missing. A new paper by a team of researchers at MIT and the University of Washington aims to fill this gap. And their findings are even more disturbing than most would hope.

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Sycophancy: the original sin of modern AI

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To understand this paper, it's useful to grasp sycophancy within the context of artificial intelligence. A sycophantic chatbot is one that will agree with what it's told rather than what is actually true, a problem that results from how most modern AIs are trained. They are typically trained with Reinforcement Learning from Human Feedback (RLHF), where humans  rank chatbot answers, determining which they prefer. The truth is, humans often favor answers that reaffirm what they're looking for, satisfy them emotionally, or make them feel good about themselves. Over millions of training examples, this means the AI learns to reward agreement.

The study highlights the growing risks associated with AI sycophancy. Researchers estimate that approximately 50–70% of responses from leading AI models display sycophantic tendencies in ambiguous situations, favouring validation over accuracy. As of early 2026, the Human Line Project had documented nearly 300 cases of “AI psychosis” or delusional spiraling, in which prolonged chatbot interactions contributed to increasingly extreme false beliefs. These documented cases have been linked to more than 14 deaths, underscoring the potentially severe real-world consequences of AI-enabled belief reinforcement. Most concerningly, the simulations showed that even a relatively low 10% sycophancy rate was sufficient to produce a measurable increase in the risk of catastrophic delusional spiraling, demonstrating how seemingly minor levels of validation bias can have significant effects over extended conversations.

As Chandra et al. (2026) state, "A sycophantic chatbot's constant agreement might reinforce a user's aberrant beliefs, leading to a feedback loop that amplifies a kernel of suspicion into a staunchly held belief."

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Enter the ideal Bayesian: the rational person who still gets fooled

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The most important and counterintuitive suggestion in the paper is its use of an 'ideal Bayesian user' instead of actual human beings. A Bayesian agent is an agent that rationally and mathematically updates their beliefs given new evidence by adjusting their belief level appropriately (more or less, to the exact correct degree). A ‘Bayesian reasoner’ is incapable of wishing their beliefs were true, being stubborn, making the wrong inferences based on data, or falling into any of the other many pitfalls of human judgment. Essentially, it's as close a model as possible to a perfect reasoner. Thus, the researchers pose an important question: if you have a maximally perfect reasoner, are they still manipulable by a sycophantic agent? Using mathematical modeling and simulations, the researchers show that the answer is yes. Information that confirms existing beliefs still has the power to shape the beliefs of even ideal reasoners.

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How does the computational model work?

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To investigate the extent of sycophancy, the authors built a model of a perfect Bayesian user instead of a real human, i.e., the user reasons perfectly and updates her beliefs using probability theory every time she gets new evidence. The model focuses on a proposition (H), like "Are vaccines safe?" or "Is this conspiracy theory true?" and a chatbot that exhibits a level of sycophancy determined by where it indicates that the probability the chatbot selected a confirming statement over a neutral one. The conversational exchange occurs in four rounds. 

1. The user states her belief about ‘H’ to the chatbot.
2. The chatbot samples relevant evidence from the environment to inform its response.
3. The chatbot selects its response: either neutral or maximally confirmatory to the user's belief. 
4. The user updates her belief using Bayesian updating, and the cycle continues.

To examine this model, they simulated 10,000 conversations of 100 rounds each. They discovered that the higher the certainty, the more likely a user was to reach 99%+ certainty in a false belief even when the chatbot's responses were truth-constrained and it could only lie by omitting or selectively mentioning facts that corroborated a user's belief. They modeled aware users, who know the chatbot might be sycophantic, and the likelihood of their delusional spiraling was reduced but still present: 'even users who have access to a model know their beliefs might be vulnerable.'

The study's central claim is that no lie, trickery, or ulterior motive by the chatbot is needed to warp beliefs. Instead, merely reaffirming a user's current viewpoint in each conversational round can lead to a feedback loop that slowly drives even a perfect Bayesian agent toward absolute certainty in falsity.

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The Limitations of Truth and Awareness

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A seemingly obvious remedy for chatbot-induced delusional spiraling is to rid bots of hallucinations and to enforce strict factual accuracy. But, as the authors point out, such safeguards alone are not enough. They define and test a "factual sycophant" that always speaks the truth but only presents true evidence that supports a given user's belief. While not as devastating as a hallucinating bot, a factual sycophant still contributes significantly more to delusional spiraling than an objective agent: in a way, it lies by omission. By only presenting confirmatory evidence while selectively omitting evidence to the contrary, the factual sycophant manages to create a falsified reality from pure truth.

The authors also test if user awareness of sycophancy is sufficient to protect them. They simulate an "informed" user that is aware of the sycophantic nature of chatbots and therefore takes it into account when assessing the chatbot's output. Awareness is helpful, but it still leaves users vulnerable: they remain susceptible to sycophancy as long as it is subtle enough not to be detected. Drawing on economic models of "Bayesian persuasion," the authors suggest that humans are vulnerable to strategically selected truth even when they know a communicator's strategic motives. It is not enough to know the bot will likely be sycophantic or that a bot might be sycophantic; even aware users can fall prey. Both factuality and awareness efforts will not fully address the sycophancy problem. 

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What this means, and what should actually be done

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The paper concludes with three succinct suggestions.

1. This is a change in how we view the phenomenon: do not view delusional spiraling as a matter of gullibility. The paper demonstrates that the problem afflicts ideal reasoners. Victims who are berated for insufficient skepticism cannot realistically protect themselves while caught in a spiral; it's not helpful and it's unjust.
2. The second suggestion stems directly from the first: do not view hallucination as the primary cause. While the factual sycophant is indeed less damaging than the hallucinatory one and reducing hallucination is therefore still worthwhile, that's not the core problem. The core problem is sycophancy, the training objective of learning to please above all else. Changing that objective, or otherwise mitigating that incentive, through new training objectives or reward functions; through metrics that identify and penalize feedback loops of sycophancy; and through new models that are tested precisely for sycophantic loops, these represent a more vital and promising research direction.
3. Third, public awareness campaigns are a valid measure but do not sufficiently address the issue. Education should continue and reduce risk. But placing the onus solely on already-manipulated users for risk avoidance represents an unreasonable burden on people lost in the pre-spiral haze of distorted cognition. Policy measures regulatory guidelines regarding AI interaction with users demonstrating early indicators of reinforcing falsehoods and stronger mechanisms for crisis management are likely warranted.

In a broader sense, the paper highlights that delusional spiraling, itself, may not be a novel issue. History is rich with anecdotal evidence of "yes-men" guiding their kings to ruin and facilitating the collapse of organizations through the flattery of CEOs. Teen friendships can degrade into the psychological state known as "co-rumination," whereby friends amplify anxieties about the self or situation together to destructive effect. Sycophancy has always been a hazard to those around it. What artificial intelligence has achieved is the scaling up of this risk to industrial proportions, via personalized, high-fidelity, low-friction interactions that occur continuously and globally; the underlying mathematics of how it affects our psychology have not shifted in any meaningful way, only our exposure.

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Conclusion

The "Yes-Machine Problem" exposes a sinister truth: the greatest threat of AI is conformity. Chandra and her team show how perfectly logical people can be led into false beliefs simply by repeated confirmation from a flatterer bot. A factually correct or informed user cannot overcome this effect. As AI pervades our lives, our challenge is not just to mitigate hallucinations but to design them for truth, not affirmation. Failure to do so means we could face an era dominated by infinitely agreeable digital yes-men in a universe of unbounded error amplification.

Based on “Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians” by Kartik Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley, and Joshua B. Tenenbaum (arXiv:2602.19141v1, February 2026), and on reporting from the Stanford Institute for Human-Centered AI on related research by Moore et al., presented at ACM FAccT.

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References:

• Chandra, K., Kleiman-Weiner, M., Ragan-Kelley, J., & Tenenbaum, J. B. (2026). Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians. arXiv preprint arXiv:2602.19141.
• Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., et al. (2023). Towards Understanding Sycophancy in Language Models. arXiv preprint arXiv:2310.13548.
• Fanous, A., Goldberg, J., Agarwal, A., Lin, J., Zhou, A., Xu, S., et al. (2025). SycEval: Evaluating LLM Sycophancy. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8, 893–900.
• Kamenica, E., & Gentzkow, M. (2011). Bayesian Persuasion. American Economic Review, 101(6), 2590–2615.
• Dohnány, S., Kurth-Nelson, Z., Spens, E., Luettgau, L., Reid, A., Gabriel, I., et al. (2025). Technological Folie à Deux: Feedback Loops Between AI Chatbots and Mental Illness. arXiv preprint arXiv:2507.19218.

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Introduction

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Picture this - you wake up one morning, check your phone, and discover that a fraudster has emptied your bank account overnight. Your first instinct is to call someone, anyone, who can stop the money from vanishing for good. For millions of Indians today, that number is 1930, the national cybercrime helpline. At a high-level review meeting in June 2026, Union Home Minister Amit Shah directed that the helpline undergo a comprehensive revamp, one that brings in artificial intelligence, multilingual support, and a stronger framework for resolving victim grievances. This is not a minor patch. It is a signal that India wants to treat cybercrime response as a serious governance priority rather than an administrative checkbox.

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The Evolution of 1930: From a Pilot Number to National Infrastructure

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The helpline’s origin lies in 155260 (Old helpline no.), launched in 2020 by the Indian Cyber Crime Coordination Centre (I4C) with the Reserve Bank of India and the banking sector, built specifically to intercept financial fraud before funds could be laundered across accounts. In 2021, it was renamed 1930 to make the number easier for citizens to recall under stress, a small but telling decision: a security architecture only works if people can remember it during a crisis. It was paired with the National Cybercrime Reporting Portal, launched in August 2019 to strengthen reporting and response mechanisms nationwide, which was later expanded to cover all categories of cybercrime after starting out limited to content-related offences. Over five years, state police forces extended 1930 into round-the-clock, multi-line operations and linked it to local cyber cells, turning a central scheme into genuinely federated infrastructure. The numbers now justify that investment: more than ₹7,000 crore has been saved nationally through the Citizen Financial Cyber Fraud Reporting and Management System, while Mumbai alone blocked or recovered nearly ₹202 crore for victims in 2025 through the helpline. What began as a pilot number has become a core node in India’s financial security architecture.

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AI and Multilingual Support as a Citizen-Centric Governance Shift

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What makes Shah’s directive significant is not the technology itself but the design philosophy it embeds. The instruction to integrate AI and multilingual support is explicitly aimed at removing language barriers and enabling faster, more efficient complaint registration across the country. For a country with no single dominant spoken language, this is not a feature addition; it is a recognition that uniform, English-or-Hindi-first service design has been quietly excluding the citizens most vulnerable to fraud. Multilingual access addresses a long-standing gap by allowing citizens from non-Hindi-speaking states to report cybercrime in their own languages, significantly broadening reach. This marks a shift away from treating digital governance as a one-size-fits-all portal and toward treating it as a service obligation that adapts to the citizen rather than the reverse, a principle with implications well beyond cybercrime reporting.

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Routing, Tracking and Escalation: Engineering Accountability into Redressal

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The proposed reforms move beyond the front-end call experience into the architecture of follow-through. AI integration is expected to improve call routing, enable faster identification of fraud patterns, and assist real-time coordination between central and state law enforcement agencies. This matters because cyber fraud is intrinsically cross-jurisdictional: a victim in one state is often defrauded through an account opened in another. Shah directed central agencies to work closely with state governments to ensure that every call received on the helpline is followed through to its logical conclusion — language that, in policy terms, is an attempt to convert a complaint-registration system into a complaint-resolution system. Intelligent routing and case tracking, if implemented well, replace ad hoc coordination between states with a traceable escalation mechanism, the missing link that has historically allowed cases to stall after the first call was logged.

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Frozen Accounts and the Procedural Burden on Victims

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No part of the revamp is more consequential for ordinary victims than the directive on bank account freezes. The problem is compounded when a cybercrime complaint is registered in one state while the frozen account sits in another, leaving legitimate account holders, sometimes innocent third parties, locked out of their own funds for weeks. Shah directed that grievances arising from the freezing of bank accounts linked to financial frauds be addressed promptly, an instruction that responds directly to a problem now before the courts Judicial scrutiny on this exact question is intensifying: the Karnataka High Court recently held that banks cannot freeze an account completely when investigating agencies have directed only a partial freeze limited to a specified amount. A national, technology-backed mechanism for resolving such freezes would convert a recurring source of citizen grievance into a procedural safeguard, addressing one of the most cited failures of the existing system.

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Reading the Reforms Within India’s Broader Cyber Resilience Strategy

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Positioned within India’s wider digital governance trajectory, the 1930 revamp fits a recognisable pattern: build foundational infrastructure first, then layer intelligence and personalisation onto it once adoption is proven. The same logic shaped Aadhaar, UPI and the Digital India programme more broadly. India has seen a sharp rise in digital financial fraud, investment scams, sextortion and phishing attacks in recent years, and the Ministry of Home Affairs’ response, expanding I4C, building specialised cybercrime units, and now investing in AI-led citizen interfaces, signals that cyber resilience is being treated less as a law-enforcement afterthought and more as a core pillar of financial-system integrity, alongside RBI and NPCI-led safeguards.

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Will These Reforms Strengthen Trust?

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The credibility of any reform lies in implementation, not announcement. Public commentary on the revamp captures this tension well: citizens have welcomed the intent while noting that earlier promises of coordination did not always translate into resolved cases, and that awareness gaps in rural India persist regardless of how sophisticated the backend becomes The 1930 revamp will be judged not by how quickly complaints are registered, an area where India already performs reasonably, but by how reliably they are closed. If AI-driven routing and a genuine national escalation mechanism reduce the gap between complaint and resolution, particularly on account freezes, the reform will have done more for citizen trust than any awareness campaign could. If implementation falters at the state-bank coordination layer, the technology will simply make an old problem move faster without making it smaller.

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Conclusion

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The story of 1930 is the story of Indian digital governance maturing in real time: from a hastily assembled fraud helpline to a piece of national financial security infrastructure now being re-engineered for scale, language diversity and accountability. Amit Shah’s directive should be read not as a single announcement but as an acknowledgment that citizen-facing systems must keep pace with the sophistication of the threats they are built to counter. Whether this becomes a genuine trust-building reform or another well-intentioned upgrade depends entirely on what happens after the press statement — in LEA’s call centres, bank back-offices and state coordination desks across the country.

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References

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1. https://www.republicworld.com/india/amit-shah-orders-major-overhaul-of-national-cybercrime-helpline-1930-calls-for-ai-upgrade-2026-06-17-128739
2.  https://the420.in/amit-shah-national-cybercrime-helpline-revamp/
3. https://inc42.com/buzz/home-minister-amit-shah-calls-for-ai-led-revamp-of-national-cybercrime-helpline/
4. https://thenewsmill.com/2026/06/amit-shah-directs-ai-upgrade-for-national-cybercrime-helpline-1930/
5. https://risingkashmir.com/national/amit-shah-reviews-national-cybercrime-helpline-1930-calls-for-ai-upgrade-12048424
6. https://www.newkerala.com/news/a/amit-shah-reviews-national-cybercrime-helpline-1930-calls-929.htm
7.  https://simple.wikipedia.org/wiki/1930_(Indian_Cybercrime_Helpline)
8. https://www.newsonair.gov.in/over-rs-7000-crore-saved-through-citizen-financial-cyber-fraud-reporting-and-management-system
9. https://the420.in/mumbai-1930-cyber-helpline-saves-202-crore-2025

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Executive Summary:

BrazenBamboo’s DEEPDATA malware represents a new wave of advanced cyber espionage tools, exploiting a zero-day vulnerability in Fortinet FortiClient to extract VPN credentials and sensitive data through fileless malware techniques and secure C2 communications. With its modular design, DEEPDATA targets browsers, messaging apps, and password stores, while leveraging reflective DLL injection and encrypted DNS to evade detection. Cross-platform compatibility with tools like DEEPPOST and LightSpy highlights a coordinated development effort, enhancing its espionage capabilities. To mitigate such threats, organizations must enforce network segmentation, deploy advanced monitoring tools, patch vulnerabilities promptly, and implement robust endpoint protection. Vendors are urged to adopt security-by-design practices and incentivize vulnerability reporting, as vigilance and proactive planning are critical to combating this sophisticated threat landscape.

Introduction

The increased use of zero-day vulnerabilities by more complex threat actors reinforces the importance of more developed countermeasures. One of the threat actors identified is BrazenBamboo uses a zero-day vulnerability in Fortinet FortiClient for Windows through the DEEPDATA advanced malware framework. This research explores technical details about DEEPDATA, the tricks used in its operations, and its other effects.

Technical Findings

1. Vulnerability Exploitation Mechanism

The vulnerability in Fortinet’s FortiClient lies in its failure to securely handle sensitive information in memory. DEEPDATA capitalises on this flaw via a specialised plugin, which:

• Accesses the VPN client’s process memory.
• Extracts unencrypted VPN credentials from memory, bypassing typical security protections.
• Transfers credentials to a remote C2 server via encrypted communication channels.

2. Modular Architecture

DEEPDATA exhibits a highly modular design, with its core components comprising:

• Loader Module (data.dll): Decrypts and executes other payloads.
• Orchestrator Module (frame.dll): Manages the execution of multiple plugins.
• FortiClient Plugin: Specifically designed to target Fortinet’s VPN client.

Each plugin operates independently, allowing flexibility in attack strategies depending on the target system.

3. Command-and-Control (C2) Communication

DEEPDATA establishes secure channels to its C2 infrastructure using WebSocket and HTTPS protocols, enabling stealthy exfiltration of harvested data. Technical analysis of network traffic revealed:

• Dynamic IP switching for C2 servers to evade detection.
• Use of Domain Fronting, hiding C2 communication within legitimate HTTPS traffic.
• Time-based communication intervals to minimise anomalies in network behavior.

4. Advanced Credential Harvesting Techniques

Beyond VPN credentials, DEEPDATA is capable of:

• Dumping password stores from popular browsers, such as Chrome, Firefox, and Edge.
• Extracting application-level credentials from messaging apps like WhatsApp, Telegram, and Skype.
• Intercepting credentials stored in local databases used by apps like KeePass and Microsoft Outlook.

5. Persistence Mechanisms

To maintain long-term access, DEEPDATA employs sophisticated persistence techniques:

• Registry-based persistence: Modifies Windows registry keys to reload itself upon system reboot.
• DLL Hijacking: Substitutes legitimate DLLs with malicious ones to execute during normal application operations.
• Scheduled Tasks and Services: Configures scheduled tasks to periodically execute the malware, ensuring continuous operation even if detected and partially removed.

Additional Tools in BrazenBamboo’s Arsenal

1. DEEPPOST

A complementary tool used for data exfiltration, DEEPPOST facilitates the transfer of sensitive files, including system logs, captured credentials, and recorded user activities, to remote endpoints.

2. LightSpy Variants

• The Windows variant includes a lightweight installer that downloads orchestrators and plugins, expanding espionage capabilities across platforms.
• Shellcode-based execution ensures that LightSpy’s payload operates entirely in memory, minimising artifacts on the disk.

3. Cross-Platform Overlaps

BrazenBamboo’s shared codebase across DEEPDATA, DEEPPOST, and LightSpy points to a centralised development effort, possibly linked to a Digital Quartermaster framework. This shared ecosystem enhances their ability to operate efficiently across macOS, iOS, and Windows systems.

Notable Attack Techniques

1. Memory Injection and Data Extraction

Using Reflective DLL Injection, DEEPDATA injects itself into legitimate processes, avoiding detection by traditional antivirus solutions.

• Memory Scraping: Captures credentials and sensitive information in real-time.
• Volatile Data Extraction: Extracts transient data that only exists in memory during specific application states.

2. Fileless Malware Techniques

DEEPDATA leverages fileless infection methods, where its payload operates exclusively in memory, leaving minimal traces on the system. This complicates post-incident forensic investigations.

3. Network Layer Evasion

By utilising encrypted DNS queries and certificate pinning, DEEPDATA ensures that network-level defenses like intrusion detection systems (IDS) and firewalls are ineffective in blocking its communications.

Recommendations

1. For Organisations

• Apply Network Segmentation: Isolate VPN servers from critical assets.
• Enhance Monitoring Tools: Deploy behavioral analysis tools that detect anomalous processes and memory scraping activities.
• Regularly Update and Patch Software: Although Fortinet has yet to patch this vulnerability, organisations must remain vigilant and apply fixes as soon as they are released.

2. For Security Teams

• Harden Endpoint Protections: Implement tools like Memory Integrity Protection to prevent unauthorised memory access.
• Use Network Sandboxing: Monitor and analyse outgoing network traffic for unusual behaviors.
• Threat Hunting: Proactively search for indicators of compromise (IOCs) such as unauthorised DLLs (data.dll, frame.dll) or C2 communications over non-standard intervals.

3. For Vendors

• Implement Security by Design: Adopt advanced memory protection mechanisms to prevent credential leakage.
• Bug Bounty Programs: Encourage researchers to report vulnerabilities, accelerating patch development.

Conclusion

DEEPDATA is a form of cyber espionage and represents the next generation of tools that are more advanced and tunned for stealth, modularity and persistence. While Brazen Bamboo is in the process of fine-tuning its strategies, the organisations and vendors have to be more careful and be ready to respond to these tricks. The continuous updating, the ability to detect the threats and a proper plan on how to deal with incidents are crucial in combating the attacks.

References:

1. https://thehackernews.com/2024/11/warning-deepdata-malware-exploiting.html‍
2. http://www.theregister.com/2024/11/19/china_brazenbamboo_fortinet_0day/