#FactCheck- No, Iran’s Supreme Leader Mojtaba Khamenei Is Not Dead—Viral Video Debunked
Executive Summary
A video circulating on social media claims that Iran’s new Supreme Leader Mojtaba Khamenei has passed away, with users attributing the claim to American sources. However, research by the CyberPeace found the claim to be false. Our research confirms that Mojtaba Khamenei is alive and in good health.
Claim
A Facebook user shared the viral video, claiming that Iran’s new Supreme Leader Mojtaba Khamenei had died.
Fact Check
To verify the claim, we conducted keyword searches on Google but found no credible media reports confirming his death. Further research led us to a report published on April 10, 2026, by ABP News. According to the report, amid discussions around a ceasefire, Mojtaba Khamenei issued a statement saying that Iran does not seek war with the United States or Israel, but as a nation, it must defend its rights.
Additionally, the image used in the viral video was analyzed using the AI detection tool HIVE Moderation. The results indicated a 99% probability that the image is AI-generated.
Conclusion
The viral claim is false and misleading. There is no credible evidence to suggest that Mojtaba Khamenei has died. On the contrary, recent verified reports confirm that he is alive and has even issued public statements on ongoing geopolitical developments. The widespread circulation of this claim appears to be driven by misinformation, amplified through social media without verification. The use of AI-generated visuals further adds to the confusion, making the content appear authentic at first glance.
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Executive Summary:
In late 2024 an Indian healthcare provider experienced a severe cybersecurity attack that demonstrated how powerful AI ransomware is. This blog discusses the background to the attack, how it took place and the effects it caused (both medical and financial), how organisations reacted, and the final result of it all, stressing on possible dangers in the healthcare industry with a lack of sufficiently adequate cybersecurity measures in place. The incident also interrupted the normal functioning of business and explained the possible economic and image losses from cyber threats. Other technical results of the study also provide more evidence and analysis of the advanced AI malware and best practices for defending against them.
1. Introduction
The integration of artificial intelligence (AI) in cybersecurity has revolutionised both defence mechanisms and the strategies employed by cybercriminals. AI-powered attacks, particularly ransomware, have become increasingly sophisticated, posing significant threats to various sectors, including healthcare. This report delves into a case study of an AI-powered ransomware attack on a prominent Indian healthcare provider in 2024, analysing the attack's execution, impact, and the subsequent response, along with key technical findings.
2. Background
In late 2024, a leading healthcare organisation in India which is involved in the research and development of AI techniques fell prey to a ransomware attack that was AI driven to get the most out of it. With many businesses today relying on data especially in the healthcare industry that requires real-time operations, health care has become the favourite of cyber criminals. AI aided attackers were able to cause far more detailed and damaging attack that severely affected the operation of the provider whilst jeopardising the safety of the patient information.
3. Attack Execution
The attack began with the launch of a phishing email designed to target a hospital administrator. They received an email with an infected attachment which when clicked in some cases injected the AI enabled ransomware into the hospitals network. AI incorporated ransomware was not as blasé as traditional ransomware, which sends copies to anyone, this studied the hospital’s IT network. First, it focused and targeted important systems which involved implementation of encryption such as the electronic health records and the billing departments.
The fact that the malware had an AI feature allowed it to learn and adjust its way of propagation in the network, and prioritise the encryption of most valuable data. This accuracy did not only increase the possibility of the potential ransom demand but also it allowed reducing the risks of the possibility of early discovery.
4. Impact
- The consequences of the attack were immediate and severe: The consequences of the attack were immediate and severe.
- Operational Disruption: The centralization of important systems made the hospital cease its functionality through the acts of encrypting the respective components. Operations such as surgeries, routine medical procedures and admitting of patients were slowed or in some cases referred to other hospitals.
- Data Security: Electronic patient records and associated billing data became off-limit because of the vulnerability of patient confidentiality. The danger of data loss was on the verge of becoming permanent, much to the concern of both the healthcare provider and its patients.
- Financial Loss: The attackers asked for 100 crore Indian rupees (approximately 12 USD million) for the decryption key. Despite the hospital not paying for it, there were certain losses that include the operational loss due to the server being down, loss incurred by the patients who were affected in one way or the other, loss incurred in responding to such an incident and the loss due to bad reputation.
5. Response
As soon as the hotel’s management was informed about the presence of ransomware, its IT department joined forces with cybersecurity professionals and local police. The team decided not to pay the ransom and instead recover the systems from backup. Despite the fact that this was an ethically and strategically correct decision, it was not without some challenges. Reconstruction was gradual, and certain elements of the patients’ records were permanently erased.
In order to avoid such attacks in the future, the healthcare provider put into force several organisational and technical actions such as network isolation and increase of cybersecurity measures. Even so, the attack revealed serious breaches in the provider’s IT systems security measures and protocols.
6. Outcome
The attack had far-reaching consequences:
- Financial Impact: A healthcare provider suffers a lot of crashes in its reckoning due to substantial service disruption as well as bolstering cybersecurity and compensating patients.
- Reputational Damage: The leakage of the data had a potential of causing a complete loss of confidence from patients and the public this affecting the reputation of the provider. This, of course, had an effect on patient care, and ultimately resulted in long-term effects on revenue as patients were retained.
- Industry Awareness: The breakthrough fed discussions across the country on how to improve cybersecurity provisions in the healthcare industry. It woke up the other care providers to review and improve their cyber defence status.
7. Technical Findings
The AI-powered ransomware attack on the healthcare provider revealed several technical vulnerabilities and provided insights into the sophisticated mechanisms employed by the attackers. These findings highlight the evolving threat landscape and the importance of advanced cybersecurity measures.
7.1 Phishing Vector and Initial Penetration
- Sophisticated Phishing Tactics: The phishing email was crafted with precision, utilising AI to mimic the communication style of trusted contacts within the organisation. The email bypassed standard email filters, indicating a high level of customization and adaptation, likely due to AI-driven analysis of previous successful phishing attempts.
- Exploitation of Human Error: The phishing email targeted an administrative user with access to critical systems, exploiting the lack of stringent access controls and user awareness. The successful penetration into the network highlighted the need for multi-factor authentication (MFA) and continuous training on identifying phishing attempts.
7.2 AI-Driven Malware Behavior
- Dynamic Network Mapping: Once inside the network, the AI-powered malware executed a sophisticated mapping of the hospital's IT infrastructure. Using machine learning algorithms, the malware identified the most critical systems—such as Electronic Health Records (EHR) and the billing system—prioritising them for encryption. This dynamic mapping capability allowed the malware to maximise damage while minimising its footprint, delaying detection.
- Adaptive Encryption Techniques: The malware employed adaptive encryption techniques, adjusting its encryption strategy based on the system's response. For instance, if it detected attempts to isolate the network or initiate backup protocols, it accelerated the encryption process or targeted backup systems directly, demonstrating an ability to anticipate and counteract defensive measures.
- Evasive Tactics: The ransomware utilised advanced evasion tactics, such as polymorphic code and anti-forensic features, to avoid detection by traditional antivirus software and security monitoring tools. The AI component allowed the malware to alter its code and behaviour in real time, making signature-based detection methods ineffective.
7.3 Vulnerability Exploitation
- Weaknesses in Network Segmentation: The hospital’s network was insufficiently segmented, allowing the ransomware to spread rapidly across various departments. The malware exploited this lack of segmentation to access critical systems that should have been isolated from each other, indicating the need for stronger network architecture and micro-segmentation.
- Inadequate Patch Management: The attackers exploited unpatched vulnerabilities in the hospital’s IT infrastructure, particularly within outdated software used for managing patient records and billing. The failure to apply timely patches allowed the ransomware to penetrate and escalate privileges within the network, underlining the importance of rigorous patch management policies.
7.4 Data Recovery and Backup Failures
- Inaccessible Backups: The malware specifically targeted backup servers, encrypting them alongside primary systems. This revealed weaknesses in the backup strategy, including the lack of offline or immutable backups that could have been used for recovery. The healthcare provider’s reliance on connected backups left them vulnerable to such targeted attacks.
- Slow Recovery Process: The restoration of systems from backups was hindered by the sheer volume of encrypted data and the complexity of the hospital’s IT environment. The investigation found that the backups were not regularly tested for integrity and completeness, resulting in partial data loss and extended downtime during recovery.
7.5 Incident Response and Containment
- Delayed Detection and Response: The initial response was delayed due to the sophisticated nature of the attack, with traditional security measures failing to identify the ransomware until significant damage had occurred. The AI-powered malware’s ability to adapt and camouflage its activities contributed to this delay, highlighting the need for AI-enhanced detection and response tools.
- Forensic Analysis Challenges: The anti-forensic capabilities of the malware, including log wiping and data obfuscation, complicated the post-incident forensic analysis. Investigators had to rely on advanced techniques, such as memory forensics and machine learning-based anomaly detection, to trace the malware’s activities and identify the attack vector.
8. Recommendations Based on Technical Findings
To prevent similar incidents, the following measures are recommended:
- AI-Powered Threat Detection: Implement AI-driven threat detection systems capable of identifying and responding to AI-powered attacks in real time. These systems should include behavioural analysis, anomaly detection, and machine learning models trained on diverse datasets.
- Enhanced Backup Strategies: Develop a more resilient backup strategy that includes offline, air-gapped, or immutable backups. Regularly test backup systems to ensure they can be restored quickly and effectively in the event of a ransomware attack.
- Strengthened Network Segmentation: Re-architect the network with robust segmentation and micro-segmentation to limit the spread of malware. Critical systems should be isolated, and access should be tightly controlled and monitored.
- Regular Vulnerability Assessments: Conduct frequent vulnerability assessments and patch management audits to ensure all systems are up to date. Implement automated patch management tools where possible to reduce the window of exposure to known vulnerabilities.
- Advanced Phishing Defences: Deploy AI-powered anti-phishing tools that can detect and block sophisticated phishing attempts. Train staff regularly on the latest phishing tactics, including how to recognize AI-generated phishing emails.
9. Conclusion
The AI empowered ransomware attack on the Indian healthcare provider in 2024 makes it clear that the threat of advanced cyber attacks has grown in the healthcare facilities. Sophisticated technical brief outlines the steps used by hackers hence underlining the importance of ongoing active and strong security. This event is a stark message to all about the importance of not only remaining alert and implementing strong investments in cybersecurity but also embarking on the formulation of measures on how best to counter such incidents with limited harm. AI is now being used by cybercriminals to increase the effectiveness of the attacks they make and it is now high time all healthcare organisations ensure that their crucial systems and data are well protected from such attacks.
Executive Summary
A video circulating on social media shows a woman using abusive language in front of a camera. Users sharing the clip claim that the woman is a professor at Galgotias University and that the video exposes her alleged reality. However, an research by CyberPeace found the claim to be misleading. The probe revealed that the woman seen in the viral video has no connection with Galgotias University and is not a professor there.Fact-checking further showed that the video is not recent but around seven years old. The woman featured in the clip was identified as Shubhrastha, who is a political strategist by profession.
Claim:
A user on X (formerly Twitter) shared the viral video on February 18, 2026, claiming: “A ‘class in abuse studies’ at Galgotias University? An obscene video of a professor teaching ethics has gone viral. Another shameful chapter has been added to the list of controversies surrounding Galgotias University.” The post further alleged that after falsely claiming a Chinese robot as its own, the university’s “Culture and Ethics” faculty member was seen publicly using abusive language in the viral clip. The post link and its archived version are provided below:
Fact Check:
To verify the authenticity of the viral claim, we extracted key frames from the video and conducted a reverse image search using Google Lens. During the research , we found the same video uploaded on the Indian Spectator’s YouTube channel on June 9, 2018
The video was also found on another YouTube channel, where it had been uploaded on June 12, 2018.
Conclusion
The research clearly establishes that the woman seen in the viral video has no association with Galgotias University and is not a professor there. The clip is also not recent but approximately seven years old. The woman in the video was identified as Shubhrastha, a political strategist.
Executive Summary:
Following India’s heavy defeat to South Africa in the T20 World Cup 2026, the team has been facing intense trolling on social media. Amid this backdrop, a video of Indian cricket team head coach Gautam Gambhir has gone viral. In the clip, Gambhir can be heard saying,“Even people who have nothing to do with cricket have made comments. An IPL owner also wrote about split coaching. It’s surprising. People must stay in their own domain. If we don’t interfere in someone else’s domain, they have no right to interfere in ours.”The video is being shared with the claim that Gambhir made these remarks recently in response to trolling after India’s loss to South Africa in the T20 World Cup 2026. However, research by the CyberPeace found the claim to be misleading. The viral video is not related to the T20 World Cup 2026. It is from December 2025 and pertains to India’s Test series defeat against South Africa. An old video is being circulated with a misleading context.
Claim
An Instagram user, ‘rns_news200’, shared the viral video on February 23, 2026, claiming that after the loss to South Africa, head coach Gautam Gambhir issued a stern warning to Indian fans. The caption stated that Suryakumar Yadav was heavily trolled on social media after the match, and Gambhir responded strongly, saying players should not be unfairly targeted and the team deserves support, especially during difficult times.
Fact Check
To verify the claim, we conducted a keyword search on Google. We found the same video on the official X (formerly Twitter) account of sports journalist Vikrant Gupta. The video was posted on December 7, 2025. According to the caption, Gambhir was expressing dissatisfaction following India’s performance.
We also found the longer version of the video on the official website of the Board of Control for Cricket in India (BCCI), where it was published on December 6, 2025. In the full video, Gambhir is clearly seen speaking about India’s defeat to South Africa in a Test match. The specific segment that went viral appears around the 1 minute 58 second mark.
Conclusion
Our research found that the viral claim about Gautam Gambhir’s video being linked to trolling after the T20 World Cup 2026 is misleading. The clip is from December 2025 and relates to India’s Test series defeat against South Africa — not the T20 World Cup 2026.An old video is being reshared with a false and misleading context.
