#FactCheck - Indian Men’s 4x400m Relay Team’s Record-Breaking Achievement in August 2023 Misrepresented as Recent Event

Executive Summary:

Given that AI technologies are evolving at a fast pace in 2024, an AI-oriented phishing attack on a large Indian financial institution illustrated the threats. The documentation of the attack specifics involves the identification of attack techniques, ramifications to the institution, intervention conducted, and resultant effects. The case study also turns to the challenges connected with the development of better protection and sensibilisation of automatized threats. 

Introduction

Due to the advancement in AI technology, its uses in cybercrimes across the world have emerged significant in financial institutions. In this report a serious incident that happened in early 2024 is analysed, according to which a leading Indian bank was hit by a highly complex, highly intelligent AI-supported phishing operation. Attack made use of AI’s innate characteristic of data analysis and data persuasion which led into a severe compromise of the bank’s internal structures.

Background

The chosen financial institution, one of the largest banks in India, had a good background regarding the extremity of its cybersecurity policies. However, these global cyberattacks opened up new threats that AI-based methods posed that earlier forms of security could not entirely counter efficiently. The attackers concentrated on the top managers of the bank because it is evident that controlling such persons gives the option of entering the inner systems as well as financial information. 

Attack Execution

The attackers utilised AI in sending the messages that were an exact look alike of internal messages sent between employees. From Facebook and Twitter content, blog entries, and lastly, LinkedIn connection history and email tenor of the bank’s executives, the AI used to create these emails was highly specific. Some of these emails possessed official formatting, specific internal language, and the CEO’s writing; this made them very realistic. 

 It also used that link in phishing emails that led the users to a pseudo internal portal in an attempt to obtain the login credentials. Due to sophistication, the targeted individuals thought the received emails were genuine, and entered their log in details easily to the bank’s network, thus allowing the attackers access. 

Impact

It caused quite an impact to the bank in every aspect. Numerous executives of the company lost their passwords to the fake emails and compromised several financial databases with information from customer accounts and transactions. The break-in permitted the criminals to cease a number of the financial’s internet services hence disrupting its functions and those of its customers for a number of days. 

 They also suffered a devastating blow to their customer trust because the breach revealed the bank’s weakness against contemporary cyber threats. Apart from managing the immediate operations which dealt with mitigating the breach, the financial institution was also toppling a long-term reputational hit. 

Technical Analysis and Findings

1. The AI techniques that are used in generation of the phishing emails are as follows: 

•  The attack used powerful NLP technology, which was most probably developed using the large-scaled transformer, such as GPT (Generative Pre-trained Transformer). Since these models are learned from large data samples they used the examples of the conversation pieces from social networks, emails and PC language to create quite credible emails. 

 Key Technical Features: 

•  Contextual Understanding: The AI was able to take into account the nature of prior interactions and thus write follow up emails that were perfectly in line with prior discourse. 
•  Style Mimicry: The AI replicated the writing of the CEO given the emails of the CEO and then extrapolated from the data given such elements as the tone, the language, and the format of the signature line. 
•  Adaptive Learning: The AI actively adapted from the mistakes, and feedback to tweak the generated emails for other tries and this made it difficult to detect. 

2. Sophisticated Spear-Phishing Techniques

Unlike ordinary phishing scams, this attack was phishing using spear-phishing where the attackers would directly target specific people using emails. The AI used social engineering techniques that significantly increased the chances of certain individuals replying to certain emails based on algorithms which machine learning furnished. 

 Key Technical Features: 

•  Targeted Data Harvesting: Cyborgs found out the employees of the organisation and targeted messages via the public profiles and messengers were scraped. 
•  Behavioural Analysis: The latest behaviour pattern concerning the users of the social networking sites and other online platforms were used by the AI to forecast the courses of action expected to be taken by the end users such as clicking on the links or opening of the attachments. 
•  Real-Time Adjustments: These are times when it was determined that the response to the phishing email was necessary and the use of AI adjusted the consequent emails’ timing and content. 

3. Advanced Evasion Techniques

The attackers were able to pull off this attack by leveraging AI in their evasion from the normal filters placed in emails. These techniques therefore entailed a modification of the contents of the emails in a manner that would not be easily detected by the spam filters while at the same time preserving the content of the message. 

Key Technical Features: 

•  Dynamic Content Alteration: The AI merely changed the different aspects of the email message slightly to develop several versions of the phishing email that would compromise different algorithms. 
•  Polymorphic Attacks: In this case, polymorphic code was used in the phishing attack which implies that the actual payloads of the links changed frequently, which means that it was difficult for the AV tools to block them as they were perceived as threats. 
•  Phantom Domains: Another tactic employed was that of using AI in generating and disseminating phantom domains, that are actual web sites that appear to be legitimate but are in fact short lived specially created for this phishing attack, adding to the difficulty of detection. 

4. Exploitation of Human Vulnerabilities

This kind of attack’s success was not only in AI but also in the vulnerability of people, trust in familiar language and the tendency to obey authorities. 

Key Technical Features: 

•  Social Engineering: As for the second factor, AI determined specific psychological principles that should be used in order to maximise the chance of the targeted recipients opening the phishing emails, namely the principles of urgency and familiarity. 
•  Multi-Layered Deception: The AI was successfully able to have a two tiered approach of the emails being sent as once the targeted individuals opened the first mail, later the second one by pretext of being a follow up by a genuine company/personality. 

Response

On sighting the breach, the bank’s cybersecurity personnel spring into action to try and limit the fallout. They reported the matter to the Indian Computer Emergency Response Team (CERT-In) to find who originated the attack and how to block any other intrusion. The bank also immediately started taking measures to strengthen its security a bit further, for instance, in filtering emails, and increasing the authentication procedures. 

 Knowing the risks, the bank realised that actions should be taken in order to enhance the cybersecurity level and implement a new wide-scale cybersecurity awareness program. This programme consisted of increasing the awareness of employees about possible AI-phishing in the organisation’s info space and the necessity of checking the sender’s identity beforehand.

Outcome

Despite the fact and evidence that this bank was able to regain its functionality after the attack without critical impacts with regards to its operations, the following issues were raised. Some of the losses that the financial institution reported include losses in form of compensation of the affected customers and costs of implementing measures to enhance the financial institution’s cybersecurity. However, the principle of the incident was significantly critical of the bank as customers and shareholders began to doubt the organisation’s capacity to safeguard information in the modern digital era of advanced artificial intelligence cyber threats. 

 This case depicts the importance for the financial firms to align their security plan in a way that fights the new security threats. The attack is also a message to other organisations in that they are not immune from such analysis attacks with AI and should take proper measures against such threats.

Conclusion

The recent AI-phishing attack on an Indian bank in 2024 is one of the indicators of potential modern attackers’ capabilities. Since the AI technology is still progressing, so are the advances of the cyberattacks. Financial institutions and several other organisations can only go as far as adopting adequate AI-aware cybersecurity solutions for their systems and data. 

 Moreover, this case raises awareness of how important it is to train the employees to be properly prepared to avoid the successful cyberattacks. The organisation’s cybersecurity awareness and secure employee behaviours, as well as practices that enable them to understand and report any likely artificial intelligence offences, helps the organisation to minimise risks from any AI attack.

Recommendations

1. Enhanced AI-Based Defences: Financial institutions should employ AI-driven detection and response products that are capable of mitigating AI-operation-based cyber threats in real-time. 
2.  Employee Training Programs: CYBER SECURITY: All employees should undergo frequent cybersecurity awareness training; here they should be trained on how to identify AI-populated phishing. 
3.  Stricter Authentication Protocols: For more specific accounts, ID and other security procedures should be tight in order to get into sensitive ones. 
4.  Collaboration with CERT-In: Continued engagement and coordination with authorities such as the Indian Computer Emergency Response Team (CERT-In) and other equivalents to constantly monitor new threats and valid recommendations. 
5.  Public Communication Strategies: It is also important to establish effective communication plans to address the customers of the organisations and ensure that they remain trusted even when an organisation is facing a cyber threat. 

Through implementing these, financial institutions have an opportunity for being ready with new threats that come with AI and cyber terrorism on essential financial assets in today’s complex IT environments.

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CAPTCHA, or the Completely Automated Public Turing Test to Tell Computers and Humans Apart function, is an image or distorted text that users have to identify or interpret to prove they are human. 2007 marked the inception of CAPTCHA, and Google developed its free service called reCAPTCHA, one of the most commonly used technologies to tell computers apart from humans. CAPTCHA protects websites from spam and abuse by using tests considered easy for humans but were supposed to be difficult for bots to solve. 

But, now this has changed. With AI becoming more and more sophisticated, it is now capable of solving CAPTCHA tests at a rate that is more accurate than humans, rendering them increasingly ineffective. This raises the question of whether CAPTCHA is still effective as a detection tool with the advancements of AI.

CAPTCHA Evolution: From 2007 Till Now

CAPTCHA has evolved through various versions to keep bots at bay. reCAPTCHA v1 relied on distorted text recognition, v2 introduced image-based tasks and behavioural analysis, and v3 operated invisibly, assigning risk scores based on user interactions. While these advancements improved user experience and security, AI now solves CAPTCHA with 96% accuracy, surpassing humans (50-86%). Bots can mimic human behaviour, undermining CAPTCHA’s effectiveness and raising the question: is it still a reliable tool for distinguishing real people from bots?

Smarter Bots and Their Rise

AI advancements like machine learning, deep learning and neural networks have developed at a very fast pace in the past decade, making it easier for bots to bypass CAPTCHA. They allow the bots to process and interpret the CAPTCHA types like text and images with almost human-like behaviour. Some examples of AI developments against bots are OCR or Optical Character Recognition. The earlier versions of CAPTCHA relied on distorted text: AI because of this tech is able to recognise and decipher the distorted text, making CAPTCHA useless. AI is trained on huge datasets which allows Image Recognition by identifying the objects that are specific to the question asked. These bots can mimic human habits and patterns by Behavioural Analysis and therefore fool the CAPTCHA. 

To defeat CAPTCHA, attackers have been known to use Adversarial Machine Learning, which refers to AI models trained specifically to defeat CAPTCHA. They collect CAPTCHA datasets and answers and create an AI that can predict correct answers. The implications that CAPTCHA failures have on platforms can range from fraud to spam to even cybersecurity breaches or cyberattacks. 

CAPTCHA vs Privacy: GDPR and DPDP

GDPR and the DPDP Act emphasise protecting personal data, including online identifiers like IP addresses and cookies. Both frameworks mandate transparency when data is transferred internationally, raising compliance concerns for reCAPTCHA, which processes data on Google’s US servers. Additionally, reCAPTCHA's use of cookies and tracking technologies for risk scoring may conflict with the DPDP Act's broad definition of data. The lack of standardisation in CAPTCHA systems highlights the urgent need for policymakers to reevaluate regulatory approaches.

CyberPeace Analysis: The Future of Human Verification

CAPTCHA, once a cornerstone of online security, is losing ground as AI outperforms humans in solving these challenges with near-perfect accuracy. Innovations like invisible CAPTCHA and behavioural analysis provided temporary relief, but bots have adapted, exploiting vulnerabilities and undermining their effectiveness. This decline demands a shift in focus. 

Emerging alternatives like AI-based anomaly detection, biometric authentication, and blockchain verification hold promise but raise ethical concerns like privacy, inclusivity, and surveillance. The battle against bots isn’t just about tools but it’s about reimagining trust and security in a rapidly evolving digital world.

AI is clearly winning the CAPTCHA war, but the real victory will be designing solutions that balance security, user experience and ethical responsibility. It’s time to embrace smarter, collaborative innovations to secure a human-centric internet.

References

• https://www.business-standard.com/technology/tech-news/bot-detection-no-longer-working-just-wait-until-ai-agents-come-along-124122300456_1.html 
• https://www.milesrote.com/blog/ai-defeating-recaptcha-the-evolving-battle-between-bots-and-web-security 
• https://www.technologyreview.com/2023/10/24/1081139/captchas-ai-websites-computing/‍
• https://datadome.co/guides/captcha/recaptcha-gdpr/

Introduction:

Welcome to the third edition of our blog on digital forensics series. In our previous blog we discussed the difference between copying, cloning, and imaging in the context of Digital Forensics, and found out why imaging is a better process. Today we will discuss the process of evidence collection in Digital Forensics. The whole process starts with making sure the evidence collection team has all necessary tools required for the task.

Investigating Tools and Equipment:

Below are some mentioned tools that the team should carry with them for a successful evidence collection:

• Anti-static bags
• Faraday bags
• Toolkit having screwdrivers(nonmagnetic), scissors, pins, cutters, forceps, clips etc.
• Rubber gloves
• Incident response toolkit (Software)
• Converter/Adapter: USB, SATA, IDE, SCSI
• Imaging software
• Volatile data collection tools (FTK Imager, Magnet Forensics RAM Capture)
• Pens, permanent markers
• Storage containers
• Batteries
• Video cameras
• Note/sketch pads
• Blank storage media
• Write-Blocker device
• Labels
• Crime scene security tapes
• Camera

What sources of Data are necessary for Digital Evidence?

• Hard-Drive (Desktop, Laptop, External, Server)
• Flash Drive
• SD Cards
• Floppy Disks
• Optical Media (CD, DVD)
• CCTV/DVR
• Internal Storage of Mobile Device
• GPS (Mobile/Car)
• Call Site Track (Towers)
• RAM

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Evidence Collection

The investigators encounter two primary types of evidence during the course of gathering evidence: non-electronic and electronic evidence.

The following approaches could be used to gather non-electronic evidence:

• In the course of looking into electronic crimes, recovering non-electronic evidence can be extremely important.  Be cautious to make sure that this kind of evidence is retrieved and kept safe. Items that may be relevant to a later review of electronic evidence include passwords, papers or printouts, calendars, literature, hardware and software manuals, text or graphical computer printouts, and photos.  These items should be secured and kept for further examination.
• They are frequently found close to the computer or other related hardware.  Locating, securing, and preserving all evidence is required by departmental procedures.

Three scenarios arise for the collection of digital evidence from computers: 

Situation 1: The desktop is visible, and the monitor is on.

• Take a picture of the screen and note the data that is visible. 
• Utilize tools for memory capturing to gather volatile data.
• Look for virtual disks. If so, gather mounted data's logical copies.
• Give each port and connection a label.
• Take a picture of them.
• Turn off network access to stop remote access.
• Cut off the power or turn it off.
• Locate and disconnect the hard drive by opening the CPU chassis.
• Take all evidence and place it in anti-magnetic (Faraday) bags.
• Deliver the evidence to the forensic lab.
• Keep the chain of custody intact.

Situation 2: The monitor is turned on, but it either has a blank screen (sleep mode) or an image for the screensaver.

• Make a small mouse movement (without pressing buttons). The work product should appear on the screen, or it should ask for a password.
• If moving the mouse does not result in a change to the screen, stop using the mouse and stop all keystrokes.
• Take a picture of the screen and note the data that is visible.
• Use memory capturing tools to gather volatile data (always use a write blocker to prevent manipulation during data collection).
• Proceed further in accordance with Situation 1.

Situation 3: The Monitor Is Off

• Write down the "off" status.
• After turning on the monitor, check to see if its status matches that of situations 1 or 2 above, and then take the appropriate action.
• Using a phone modem, cable, confirm that you are connected to the outside world. Try to find the phone number if there is a connection to the phone.
• To protect evidence, take out the floppy disks that might be there, package each disk separately, and label the evidence.  Put in a blank floppy disk or a seizure disk, if one is available. Avoid touching the CD drive or taking out CDs.
• Cover the power connector and every drive slot with tape.
• Note the serial number, make, and model.
• Take a picture of the computer's connections and make a diagram with the relevant cables.
• To enable precise reassembly at a later date, label all connectors and cable ends, including connections to peripheral devices. Put "unused" on any connection ports that are not in use.  Recognize docking stations for laptop computers in an attempt to locate additional storage media.
• All evidence should be seized and placed in anti-magnetic (Faraday) bags.
• All evidence should be seized and placed in anti-magnetic (Faraday) bags.
• Put a tag or label on every bag.
• Deliver the evidence to the forensic lab.
• Keep the chain of custody intact. 

Following the effective gathering of data, the following steps in the process are crucial: data packaging, data transportation, and data storage.

The following are the steps involved in data packaging, transportation, and storage:

Packaging:

• Label every computer system that is gathered so that it can be put back together exactly as it was found

When gathering evidence at a scene of crime,

• Before packing, make sure that every piece of evidence has been appropriately  labeled and documented.
• Latent or trace evidence requires particular attention, and steps should be taken to preserve it.
• Use paper or antistatic plastic bags for packing magnetic media to prevent static electricity. Do not use materials like regular plastic bags (instead use faraday bags) that can cause static electricity. 
• Be careful not to bend, fold, computer media like tapes, or CD-ROM.
• Make sure that the labels on every container used to store evidence are correct.

Transporting

• Make sure devices are not packed in containers and are safely fastened inside the car to avoid shock and excessive vibrations. Computers could be positioned on the floor of the car,and monitors could be mounted on the seat with the screen down .

When transporting evidence—

• Any electronic evidence should be kept away from magnetic sources.  Radiation transmitters, speaker magnets, and heated seats are a few examples of items that can contaminate electronic evidence.
• Avoid leaving electronic evidence in your car for longer than necessary. Electronic devices can be harmed by extremes in temperature, humidity.
• Maintain the integrity of the chain of custody while transporting any evidence.

Storing

• Evidence should be kept safe and away from extremes in humidity and temperature. Keep it away from dust, moisture, magnetic devices, and other dangerous impurities.  Be advised that extended storage may cause important evidence—like dates, times, and system configurations—to disappear. Because batteries have a finite lifespan, data loss may occur if they malfunction. Whenever the battery operated device needs immediate attention, it should be informed to the relevant authority (eg., the chief of laboratory, the forensic examiner, and the custodian of the evidence). 

CONCLUSION:

Thus, securing the crime scene to packaging, transportation and storage of data are the important steps in the process of collecting digital evidence in forensic investigations. Keeping the authenticity during the process along with their provenance is critical during this phase. It is also important to ensure the admissibility of evidence in legal proceedings. This systematic approach is essential for effectively investigating and prosecuting digital crimes.

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