#FactCheck - Debunked: Viral Video Falsely Claims Allu Arjun Joins Congress Campaign

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

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Executive Summary:

Viral pictures featuring US Secret Service agents smiling while protecting former President Donald Trump during a planned attempt to kill him in Pittsburgh have been clarified as photoshopped pictures. The pictures making the rounds on social media were produced by AI-manipulated tools. The original image shows no smiling agents found on several websites. The event happened with Thomas Mathew Crooks firing bullets at Trump at an event in Butler, PA on July 13, 2024. During the incident one was deceased and two were critically injured. The Secret Service stopped the shooter, and circulating photos in which smiles were faked have stirred up suspicion. The verification of the face-manipulated image was debunked by the CyberPeace Research Team.

Claims:

Viral photos allegedly show United States Secret Service agents smiling while rushing to protect former President Donald Trump during an attempted assassination in Pittsburgh, Pennsylvania.

Fact Check:

Upon receiving the posts, we searched for any credible source that supports the claim made, we found several articles and images of the incident but in those the images were different. 

This image was published by CNN news media, in this image we can see the US Secret Service protecting Donald Trump but not smiling. We then checked for AI Manipulation in the image using the AI Image Detection tool, True Media.

We then checked with another AI Image detection tool named, contentatscale AI image detection, which also found it to be AI Manipulated.

Comparison of both photos:

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Hence, upon lack of credible sources and detection of AI Manipulation concluded that the image is fake and misleading.

Conclusion:

The viral photos claiming to show Secret Service agents smiling when protecting former President Donald Trump during an assassination attempt have been proven to be digitally manipulated. The original image found on CNN Media shows no agents smiling. The spread of these altered photos resulted in misinformation. The CyberPeace Research Team's investigation and comparison of the original and manipulated images confirm that the viral claims are false.

• Claim: Viral photos allegedly show United States Secret Service agents smiling while rushing to protect former President Donald Trump during an attempted assassination in Pittsburgh, Pennsylvania.
• Claimed on: X, Thread
• Fact Check: Fake & Misleading

The United Nations in December 2019 passed a resolution that established an open-ended ad hoc committee. This committee was tasked to develop a ‘comprehensive international convention on countering the use of ICTs for criminal purposes’. The UN Convention on Cybercrime is an initiative of the UN member states to foster the principles of international cooperation and establish legal frameworks to provide mechanisms for combating cybercrime. The negotiations for the convention had started in early 2022. It became the first binding international criminal justice treaty to have been negotiated in over 20 years upon its adoption by the UN General Assembly.  

This convention addresses the limitations of the Budapest Convention on Cybercrime by encircling a broader range of issues and perspectives from the member states. The UN Convention against Cybercrime will open for signature at a formal ceremony hosted in Hanoi, Viet Nam, in 2025. The convention will finally enter into force 90 days after being ratified by the 40th signatory.

Objectives and Features of the Convention

• The UN Convention against Cybercrime addresses various aspects of cybercrime. These include prevention, investigation, prosecution and international cooperation. 
• The convention aims to establish common standards for criminalising cyber offences. These include offences like hacking, identity theft, online fraud, distribution of illegal content, etc. It outlines procedural and technical measures for law enforcement agencies for effective investigation and prosecution while ensuring due process and privacy protection. 
• Emphasising the importance of cross-border collaboration among member states, the convention provides mechanisms for mutual legal assistance, extradition and sharing of information and expertise. The convention aims to enhance the capacity of developing countries to combat cybercrime through technical assistance, training, and resources. 
• It seeks to balance security measures with the protection of fundamental rights. The convention highlights the importance of safeguarding human rights and privacy in cybercrime investigations and enforcement.
• The Convention emphasises the importance of prevention through awareness campaigns, education, and the promotion of a culture of cybersecurity. It encourages collaborations through public-private partnerships to enhance cybersecurity measures and raise awareness, such as protecting vulnerable groups like children, from cyber threats and exploitation.

Key Provisions of the UN Cybercrime Convention

Some key provisions of the Convention are as follows:

• The convention differentiates cyber-dependent crimes like hacking from cyber-enabled crimes like online fraud. It defines digital evidence and establishes standards for its collection, preservation, and admissibility in legal proceedings.
• It defines offences against confidentiality, integrity, and availability of computer data and includes unauthorised access, interference with data, and system sabotage. Further, content-related offences include provisions against distributing illegal content, such as CSAM and hate speech. It criminalises offences like identity theft, online fraud and intellectual property violations.
• LEAs are provided with tools for electronic surveillance, data interception, and access to stored data, subject to judicial oversight. It outlines the mechanisms for cross-border investigations, extradition, and mutual legal assistance.
• The establishment of a central body to coordinate international efforts, share intelligence, and provide technical assistance includes the involvement of experts from various fields to advise on emerging threats, legal developments, and best practices.

Comparisons with the Budapest Convention

The Budapest Convention was adopted by the Committee of Ministers of the Council of Europe at the 109th Session on 8 November 2001. This Convention was the first international treaty that addressed internet and computer crimes. A comparison between the two Conventions is as follows:

• The global participation in the UNCC is inclusive of all UN member states whereas the latter had primarily European with some non-European signatories. 
• The scope of the UNCC is broader and covers a wide range of cyber threats and cybercrimes, whereas the Budapest convention is focused on specific offences like hacking and fraud. 
• UNCC strongly focuses on privacy and human rights protections and the Budapest Convention had limited focus on human rights.
• UNCC has extensive provisions for assistance to developing countries and this is in contrast to the Budapest Convention which did not focus much on capacity building.

Future Outlook

The development of the UNCC was a complex process. The diverse views on key issues have been noted and balancing different legal systems, cultural perspectives and policy priorities has been a challenge. The rapid technology evolution that is taking place requires the Convention to be adaptable to effectively address emerging cyber threats. Striking a balance remains a critical concern. The Convention aims to provide a blended approach to tackling cybercrime by addressing the needs of countries, both developed and developing.

Conclusion

The resolution containing the UN Convention against Cybercrime is a step in global cooperation to combat cybercrime. It was adopted without a vote by the 193-member General Assembly and is expected to enter into force 90 days after ratification by the 40th signatory. The negotiations and consultations are finalised for the Convention and it is open for adoption and ratification by member states. It seeks to provide a comprehensive legal framework that addresses the challenges posed by cyber threats while respecting human rights and promoting international collaboration. 

References

• https://consultation.dpmc.govt.nz/un-cybercrime-convention/principlesandobjectives/supporting_documents/Background.pdf 
• https://news.un.org/en/story/2024/12/1158521 
• https://www.interpol.int/en/News-and-Events/News/2024/INTERPOL-welcomes-adoption-of-UN-convention-against-cybercrime#:~:text=The%20UN%20convention%20establishes%20a,and%20grooming%3B%20and%20money%20laundering 
• https://www.cnbctv18.com/technology/united-nations-adopts-landmark-global-treaty-to-combat-cybercrime-19529854.htm