#FactCheck - AI-Generated Image Falsely Shows Tamil Nadu CM Vijay Touching Rahul Gandhi’s Feet

Introduction
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Generative AI, particularly deepfake technology, poses significant risks to security in the financial sector. Deepfake technology can convincingly mimic voices, create lip-sync videos, execute face swaps, and carry out other types of impersonation through tools like DALL-E, Midjourney, Respeecher, Murf, etc, which are now widely accessible and have been misused for fraud. For example, in 2024, cybercriminals in Hong Kong used deepfake technology to impersonate the Chief Financial Officer of a company, defrauding it of $25 million. Surveys, including Regula’s Deepfake Trends 2024 and Sumsub reports, highlight financial services as the most targeted sector for deepfake-induced fraud.

Deepfake Technology and Its Risks to Financial Systems

India’s financial ecosystem, including banks, NBFCs, and fintech companies, is leveraging technology to enhance access to credit for households and MSMEs. The country is a leader in global real-time payments and its digital economy comprises 10% of its GDP.  However, it faces unique cybersecurity challenges. According to the RBI’s 2023-24 Currency and Finance report, banks cite cybersecurity threats, legacy systems, and low customer digital literacy as major hurdles in digital adoption. Deepfake technology intensifies risks like:

• Social Engineering Attacks: Information security breaches through phishing, vishing, etc. become more convincing with deepfake imagery and audio.
• Bypassing Authentication Protocols: Deepfake audio or images may circumvent voice and image-based authentication systems, exposing sensitive data.
• Market Manipulation: Misleading deepfake content making false claims and endorsements can harm investor trust and damage stock market performance.
• Business Email Compromise Scams: Deepfake audio can mimic the voice of a real person with authority in the organization to falsely authorize payments.
• Evolving Deception Techniques: The usage of AI will allow cybercriminals to deploy malware that can adapt in real-time to carry out phishing attacks and inundate targets with increased speed and variations. Legacy security frameworks are not suited to countering automated attacks at such a scale.

Existing Frameworks and Gaps

In 2016, the RBI introduced cybersecurity guidelines for banks, neo-banking, lending, and non-banking financial institutions, focusing on resilience measures like Board-level policies, baseline security standards, data leak prevention, running penetration tests, and mandating Cybersecurity Operations Centres (C-SOCs). It also mandated incident reporting to the RBI for cyber events. Similarly, SEBI’s Cybersecurity and Cyber Resilience Framework (CSCRF) applies to regulated entities (REs) like stock brokers, mutual funds, KYC agencies, etc., requiring policies, risk management frameworks, and third-party assessments of cyber resilience measures. While both frameworks are comprehensive, they require updates addressing emerging threats from generative AI-driven cyber fraud.

Cyberpeace Recommendations 

• AI Cybersecurity to Counter AI Cybercrime: AI-generated attacks can be designed to overwhelm with their speed and scale. Cybercriminals increasingly exploit platforms like LinkedIn, Microsoft Teams, and Messenger, to target people.  More and more organizations of all sizes will have to use AI-based cybersecurity for detection and response since generative AI is becoming increasingly essential in combating hackers and breaches.
• Enhancing Multi-factor Authentication (MFA):  With improving image and voice-generation/manipulation technologies, enhanced authentication measures such as token-based authentication or other hardware-based measures, abnormal behaviour detection, multi-device push notifications, geolocation verifications, etc. can be used to improve prevention strategies.  New targeted technological solutions for content-driven authentication can also be implemented.
• Addressing Third-Party Vulnerabilities: Financial institutions often outsource operations to vendors that may not follow the same cybersecurity protocols, which can introduce vulnerabilities. Ensuring all parties follow standardized protocols can address these gaps.
• Protecting Senior Professionals: Senior-level and high-profile individuals at organizations are at a greater risk of being imitated or impersonated since they hold higher authority over decision-making and have greater access  to sensitive information. Protecting their identity metrics through technological interventions is of utmost importance.
• Advanced Employee Training:  To build organizational resilience, employees must be trained to understand how generative and emerging technologies work. A well-trained workforce can significantly lower the likelihood of successful human-focused human-focused cyberattacks like phishing and impersonation.
• Financial Support to Smaller Institutions: Smaller institutions may not have the resources to invest in robust long-term cybersecurity solutions and upgrades. They require financial and technological support from the government to meet requisite standards.

Conclusion

According to The India Cyber Threat Report 2025 by the Data Security Council of India (DSCI) and Seqrite, deepfake-enabled cyberattacks, especially in the finance and healthcare sectors, are set to increase in 2025. This has the potential to disrupt services, steal sensitive data, and exploit geopolitical tensions, presenting a significant risk to the critical infrastructure of India. 

As the threat landscape changes,  institutions will have to continue to embrace AI and Machine Learning (ML) for threat detection and response. The financial sector must prioritize robust cybersecurity strategies, participate in regulation-framing procedures, adopt AI-based solutions, and enhance workforce training, to safeguard against AI-enabled fraud. Collaborative efforts among policymakers, financial institutions, and technology providers will be essential to strengthen defenses.

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Sources 

• https://sumsub.com/newsroom/deepfake-cases-surge-in-countries-holding-2024-elections-sumsub-research-shows/ 
• https://www.globenewswire.com/news-release/2024/10/31/2972565/0/en/Deepfake-Fraud-Costs-the-Financial-Sector-an-Average-of-600-000-for-Each-Company-Regula-s-Survey-Shows.html 
• https://www.sipa.columbia.edu/sites/default/files/2023-05/For%20Publication_BOfA_PollardCartier.pdf 
• https://edition.cnn.com/2024/02/04/asia/deepfake-cfo-scam-hong-kong-intl-hnk/index.html 
• https://www.rbi.org.in/Commonman/English/scripts/Notification.aspx?Id=1721 
• https://elplaw.in/leadership/cybersecurity-and-cyber-resilience-framework-for-sebi-regulated-entities/ 
• https://economictimes.indiatimes.com/tech/artificial-intelligence/ai-driven-deepfake-enabled-cyberattacks-to-rise-in-2025-healthcarefinance-sectors-at-risk-report/articleshow/115976846.cms?from=mdr 

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Executive Summary:

Traditional Business Email Compromise(BEC) attacks have become smarter, using advanced technologies to enhance their capability. Another such technology which is on the rise is WormGPT, which is a generative AI tool that is being leveraged by the cybercriminals for the purpose of BEC. This research aims at discussing WormGPT and its features as well as the risks associated with the application of the WormGPT in criminal activities. The purpose is to give a general overview of how WormGPT is involved in BEC attacks and give some advice on how to prevent it.

 Introduction

BEC(Business Email Compromise) in simple terms can be defined as a kind of cybercrime whereby the attackers target the business in an effort to defraud through the use of emails. Earlier on, BEC attacks were executed through simple email scams and phishing. However, in recent days due to the advancement of AI tools like WormGPT such malicious activities have become sophisticated and difficult to identify. This paper seeks to discuss WormGPT, a generative artificial intelligence, and how it is used in the BEC attacks to make the attacks more effective.

What is WormGPT?

Definition and Overview

WormGPT is a generative AI model designed to create human-like text. It is built on advanced machine learning algorithms, specifically leveraging large language models (LLMs). These models are trained on vast amounts of text data to generate coherent and contextually relevant content. WormGPT is notable for its ability to produce highly convincing and personalised email content, making it a potent tool in the hands of cybercriminals.

How WormGPT Works

1. Training Data: Here the WormGPT is trained with the arrays of data sets, like emails, articles, and other writing material. This extensive training enables it to understand and to mimic different writing styles and recognizable textual content.

 2. Generative Capabilities: Upon training, WormGPT can then generate text based on specific prompts, as in the following examples in response to prompts. For example, if a cybercriminal comes up with a prompt concerning the company’s financial information, WormGPT is capable of releasing an appearance of a genuine email asking for more details.

 3. Customization: WormGPT can be retrained any time with an industry or an organisation of interest in mind. This customization enables the attackers to make their emails resemble the business activities of the target thus enhancing the chances for an attack to succeed.

Enhanced Phishing Techniques

Traditional phishing emails are often identifiable by their generic and unconvincing content. WormGPT improves upon this by generating highly personalised and contextually accurate emails. This personalization makes it harder for recipients to identify malicious intent.

Automation of Email Crafting

Previously, creating convincing phishing emails required significant manual effort. WormGPT automates this process, allowing attackers to generate large volumes of realistic emails quickly. This automation increases the scale and frequency of BEC attacks.

Exploitation of Contextual Information

WormGPT can be fed with contextual information about the target, such as recent company news or employee details. This capability enables the generation of emails that appear highly relevant and urgent, further deceiving recipients into taking harmful actions.

Implications for Cybersecurity

Challenges in Detection

The use of WormGPT complicates the detection of BEC attacks. Traditional email security solutions may struggle to identify malicious emails generated by advanced AI, as they can closely mimic legitimate correspondence. This necessitates the development of more sophisticated detection mechanisms.

Need for Enhanced Training

Organisations must invest in training their employees to recognize signs of BEC attacks. Awareness programs should emphasise the importance of verifying email requests for sensitive information, especially when such requests come from unfamiliar or unexpected sources.

Implementation of Robust Security Measures

1. Multi-Factor Authentication (MFA): MFA can add an additional layer of security, making it harder for attackers to gain unauthorised access even if they successfully deceive an employee.
2. Email Filtering Solutions: Advanced email filtering solutions that use AI and machine learning to detect anomalies and suspicious patterns can help identify and block malicious emails.
3. Regular Security Audits: Conducting regular security audits can help identify vulnerabilities and ensure that security measures are up to date.

Case Studies

Case Study 1: Financial Institution

A financial institution fell victim to a BEC attack orchestrated using WormGPT. The attacker used the tool to craft a convincing email that appeared to come from the institution’s CEO, requesting a large wire transfer. The email’s convincing nature led to the transfer of funds before the scam was discovered.

Case Study 2: Manufacturing Company

In another instance, a manufacturing company was targeted by a BEC attack using WormGPT. The attacker generated emails that appeared to come from a key supplier, requesting sensitive business information. The attack exploited the company’s lack of awareness about BEC threats, resulting in a significant data breach.

Recommendations for Mitigation

1. Strengthen Email Security Protocols: Implement advanced email security solutions that incorporate AI-driven threat detection.
2. Promote Cyber Hygiene: Educate employees on recognizing phishing attempts and practising safe email habits.
3. Invest in AI for Defense: Explore the use of AI and machine learning in developing defences against generative AI-driven attacks.
4. Implement Verification Procedures: Establish procedures for verifying the authenticity of sensitive requests, especially those received via email.

Conclusion

WormGPT is a new tool in the arsenal of cybercriminals which improved their options to perform Business Email Compromise attacks more effectively and effectively. Therefore, it is critical to provide the defence community with information regarding the potential of WormGPT and its implications for enhancing the threat landscape and strengthening the protection systems against advanced and constantly evolving threats. 

This means the development of rigorous security protocols, general awareness of security solutions, and incorporating technologies such as artificial intelligence to mitigate the risk factors that arise from generative AI tools to the best extent possible.

Executive Summary:

A photo claiming that Mr. Rowan Atkinson, the famous actor who played the role of Mr. Bean, lying sick on bed is circulating on social media. However, this claim is false. The image is a digitally altered picture of Mr.Barry Balderstone from Bollington, England, who died in October 2019 from advanced Parkinson’s disease. Reverse image searches and media news reports confirm that the original photo is of Barry, not Rowan Atkinson. Furthermore, there are no reports of Atkinson being ill; he was recently seen attending the 2024 British Grand Prix. Thus, the viral claim is baseless and misleading.

Claims:

A viral photo of Rowan Atkinson aka Mr. Bean, lying on a bed in sick condition.

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Fact Check:

When we received the posts, we first did some keyword search based on the claim made, but no such posts were found to support the claim made.Though, we found an interview video where it was seen Mr. Bean attending F1 Race on July 7, 2024. 

Then we reverse searched the viral image and found a news report that looked similar to the viral photo of Mr. Bean, the T-Shirt seems to be similar in both the images.

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The man in this photo is Barry Balderstone who was a civil engineer from Bollington, England, died in October 2019 due to advanced Parkinson’s disease. Barry received many illnesses according to the news report and his application for extensive healthcare reimbursement was rejected by the East Cheshire Clinical Commissioning Group.

Taking a cue from this, we then analyzed the image in an AI Image detection tool named, TrueMedia. The detection tool found the image to be AI manipulated. The original image is manipulated by replacing the face with Rowan Atkinson aka Mr. Bean.

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Hence, it is clear that the viral claimed image of Rowan Atkinson bedridden is fake and misleading. Netizens should verify before sharing anything on the internet.

Conclusion:

Therefore, it can be summarized that the photo claiming Rowan Atkinson in a sick state is fake and has been manipulated with another man’s image. The original photo features Barry Balderstone, the man who was diagnosed with stage 4 Parkinson’s disease and subsequently died in 2019. In fact, Rowan Atkinson seemed perfectly healthy recently at the 2024 British Grand Prix. It is important for people to check on the authenticity before sharing so as to avoid the spreading of misinformation.

• Claim: A Viral photo of Rowan Atkinson aka Mr. Bean, lying on a bed in a sick condition.
• Claimed on: X, Facebook
• Fact Check: Fake & Misleading