Saturday, 20 July 2024

With great kernel power, comes great operating responsibility!

The recent Microsoft-Crowdstrike incident causing Windows Blue Screen Of Death error, is the result of an update pushed to their Falcon sensor version 6.58. This version was pulled after widespread reports of BSOD incidents.

However, this incident raises several critical questions about the root cause, the testing and deployment processes, the capabilities and shortcomings of CrowdStrike's tools, and the oversight mechanisms in place.


The issue is linked to the sensor's interaction with the Windows operating system at the kernel level. CrowdStrike's sensors operate at this level to provide deep security insights and to prevent sophisticated attacks that might otherwise bypass user-level protections. By integrating at the kernel level, these sensors can monitor and respond to system calls and processes in real-time, offering robust security measures against advanced threats.

However, kernel-level modifications come with significant risks. Any error or incompatibility in the kernel-mode drivers can lead to critical system failures, like BSODs. In this case, the specific problem likely arose from an unintended conflict or bug within the sensor's driver code, which directly interacts with the Windows kernel.

Root Cause

The root cause of the Windows host crashes was identified as a defect in a single content update for the Falcon sensor. The problematic update, specifically the "C-00000291*.sys" file, caused the Windows OS to crash. CrowdStrike's engineering team suggested to revert the changes to a previous stable version of the channel file.

Lack of Thorough Testing

One of the primary issues highlighted by this incident is the apparent lack of thorough testing in a controlled test environment before deploying the update to production. Proper testing procedures are crucial to ensure that any updates or changes do not adversely affect the system's stability and functionality. The failure to identify such a critical issue in the testing phase suggests that the update was either inadequately tested or not tested in an environment that accurately mirrored the production setup.

Capabilities and Shortcomings of CrowdStrike Falcon Tools

Apparently, the next-gen advanced threat detection and prevention capabilities of Crowdstrike, with this incident underscores some significant shortcomings:

Strengths

  • Advanced Threat Detection: Falcon is equipped with robust machine learning and behavioral analytics to detect and prevent threats.
  • Cloud-Based Architecture: The cloud-based platform allows for real-time threat intelligence and updates.
  • Scalability: Falcon can scale to protect large enterprises with numerous endpoints.

Shortcomings

  • Update Management: The incident revealed weaknesses in the update management process, particularly in testing and validation.
  • Oversight and Quality Control: The lack of oversight in ensuring the quality and stability of updates before deployment is a critical flaw.
  • Customer Impact: The rapid deployment of untested updates directly impacted customer operations, leading to significant downtime and disruption.

Lack of Security Standards and Process Controls

The incident highlights a broader issue of insufficient security standards and process controls in place to prevent such configuration or administration errors. Effective security practices should include:

  • Comprehensive Testing: Updates should undergo rigorous testing in environments that replicate production setups.
  • Change Management: A robust change management process should be implemented to ensure that any updates are carefully reviewed and approved.
  • Incident Response: Clear incident response procedures should be in place to quickly address and mitigate any issues that arise from updates.
People talk highest levels of quality but have lowest levels of realistic implementation, This reflects gaps between process vs practical adoption. The level of seriousness is not reflected when it boils down to nth level worker.

Microsoft's Oversight Responsibilities

Microsoft, as the provider of the Windows operating system, shares a degree of responsibility in ensuring that third-party integrations, such as those from CrowdStrike, do not compromise system stability. The delegation of control to third-party vendors without adequate oversight can lead to such incidents.

Recommendations for Microsoft
  • Stricter Integration Policies: Implement stricter policies and guidelines for third-party integrations to ensure compatibility and stability.
  • Joint Testing Initiatives: Collaborate with third-party vendors to conduct joint testing and validation of updates.
  • Monitoring and Auditing: Regularly monitor and audit third-party integrations to identify and address potential issues proactively.

CrowdStrike's Accountability

CrowdStrike must take responsibility for the failure and implement measures to prevent recurrence. The company needs to address several critical areas:

Improving Update Testing
  • Enhanced Testing Protocols: Develop and enforce stringent testing protocols for updates.
  • Simulated Production Environments: Use simulated production environments to test updates thoroughly.
  • Beta Programs: Introduce beta testing programs where updates are tested by a small group of users before wider deployment.
Strengthening Quality Control
  • Quality Assurance Teams: Establish dedicated quality assurance teams to review and approve updates.
  • Automated Testing Tools: Utilize automated testing tools to identify potential issues quickly.
Customer Communication
  • Transparent Communication: Maintain transparent communication with customers about updates and potential issues.
  • Support Channels: Ensure robust support channels are available for customers to report and resolve issues promptly.
Great minds can have great ideas but if they do not bring it with customer lens and accountability it will be only hyped-up product security. 

Conclusion

This incident clearly calls out the critical gaps in following basic security guidelines of update testing and deployment processes, both within CrowdStrike and in Microsoft. While CrowdStrike offers powerful cybersecurity tools, the incident underscores the importance of rigorous testing, quality control, and effective communication with customers. 

Moving forward, both CrowdStrike and Microsoft must implement stronger safeguards to prevent such incidents and ensure the stability and security of their systems. 

Don't strike the wrong places to loose your market for competition!!!




Sunday, 14 July 2024

Enterprise Responsible AI Adoption – A Holistic AI Perspective

Enterprise Trade-off: Enterprises can use multiple open-source models to achieve around 90% accuracy, compared to using the latest OpenAI model and achieving 95% accuracy with a single model. Open-source models also require additional training and Reinforcement Learning from Human Feedback (RLHF). The trade-off between achieving 60% accuracy with open-source versus 90% with proprietary models needs careful evaluation.

  • Model and Data Alignment: Failing to invest time in understanding the models, aligning them with the right data, and establishing proper benchmarks will lead to a random, fragile implementation. A "lift-and-shift" approach to building AI products is not a sustainable strategy.
  • Data and Model Understanding: If you don’t fully understand the data sources and the limitations of the models you're using, don’t assume that handling only the happy path scenarios is enough to deliver successful GenAI applications.
  • Responsible AI Adoption: Relying on open-source models that deliver subpar accuracy does not constitute responsible AI adoption. It reflects a short-term vision and a failure to prioritize long-term sustainability.
  • Open Source Paradox: There's a growing push to leverage open-source models and frameworks, but expectations for state-of-the-art accuracy remain unrealistically high.
  • Long-term Costs: The broader impact and cost of fixing data issues or model errors are often overlooked in favor of flashy, short-term demo solutions that generate applause but don't provide lasting value.

Key Questions to Ask About the Model:
  1. Data: Is the data representative, reliable, and aligned with the intended use case?
  2. Domain: Does the model have domain-specific knowledge to perform effectively?
  3. Benchmark: Have clear benchmarks and performance metrics been set and evaluated?
  4. Key Questions to Ask About the Use Case:
  5. Why do we need an LLM?: Is an LLM the best solution for this problem, or are there alternatives?
  6. How much effort does it save?: What quantifiable efficiencies or cost savings does the LLM offer compared to traditional methods?
  7. What is the plan to improve accuracy?: How will you progress from the current accuracy level, and what steps will be taken to continuously improve the model's performance?
  8. Leadership Clarity: Leaders must understand that simply purchasing a platform or tool will not solve the underlying challenges of responsible AI adoption. A clear vision and strategy are critical for long-term success.

Sunday, 9 June 2024

Uncovering the Snowflake Data Breach: Causes, Impacts, and Lessons Learned

Snowflake Data Breach Incident Overview: 

In May 2024, Snowflake disclosed a cyber incident affecting several of its clients, including Ticketmaster and Santander, which resulted in the exposure of sensitive data. The breach stemmed from credential stuffing attacks, targeting accounts with single-factor authentication​.

After Snowflake announced its initial data breach, several subsequent reports and analyses emerged, detailing ongoing impacts and further developments related to the incident. Here is a comprehensive report on the massive data breach incident:

Timeline Study:

  • October 2023: Initial compromise occurred through an employee's ServiceNow account using credentials obtained via the Lumma Stealer malware.
  • May 20, 2024: Live Nation (Ticketmaster's parent company) identified unauthorized activity. Live Nation, confirmed a data breach after its Snowflake account was found compromised​.
  • May 23, 2024: Advance Auto Parts was reported to have had 3TB of data stolen from its Snowflake cloud storage environment, including customer profiles, orders, and sensitive employee information​.
  • May 23, 2024: Threat actor "Whitewarlock" posted Santander data for sale.
  • May 27, 2024: Threat actor "ShinyHunters" offered Ticketmaster data for sale.
  • June 1, 2024: Hudson Rock, the cybersecurity firm that initially reported the breach, took down their report following legal pressure from Snowflake. Despite this, ongoing analyses suggested the compromise involved stolen credentials used to bypass security measures​.
  • June 2, 2024: Snowflake released an official statement confirming the incident and mitigation steps​
  • May 14, 2024: Santander Bank disclosed unauthorized access to one of its databases hosted by a third-party provider, affecting customers and employees in Chile, Spain, and Uruguay​.
  • June 3, 2024: Further details emerged about the breadth of the breach, indicating that the attackers targeted multiple high-profile companies and sought a $20 million ransom from Snowflake​.
  • June 5, 2024: Reports confirmed the sale of stolen data from Advance Auto Parts on hacking forums, corroborating earlier claims of significant data exfiltration from Snowflake’s customer environments​.
These reports indicate that the breach involved a mix of stolen credentials and weak security practices on the part of some Snowflake customers. Snowflake has maintained that the breach was not due to a vulnerability in its platform but rather resulted from compromised customer credentials​.




Probable Cause Analysis:
  • The breach occurred due to credential stuffing attacks exploiting accounts with single-factor authentication.
  • Stolen credentials were used to access demo accounts not protected by Okta or MFA (Multi-Factor Authentication)​.
Accountability:
  • Snowflake confirmed no vulnerabilities or misconfigurations in their platform but acknowledged that compromised credentials of a former employee were used.
  • Criticism arose due to the lack of MFA on demo accounts and failure to disable access for a former employee​.
Impact:
  • Personal information of over 560 million Ticketmaster users and data from Santander, including bank account details and credit card numbers, were compromised.
  • Potential impacts included identity theft, financial fraud, and other malicious activities​.
Remediations:
  • Snowflake advised immediate implementation of MFA across all accounts.
  • Organizations were recommended to reset and rotate Snowflake credentials, and enforce network policy rules to restrict access to trusted locations only.
  • Snowflake provided Indicators of Compromise (IoCs) and collaborated with CrowdStrike and Mandiant for a thorough investigation​.

SEC Filings (Form 8-K and 10-K) Summary:

Form 8-K:
  • Snowflake's 8-K filing detailed the breach, emphasizing the credential stuffing attack and steps taken to mitigate further risks.
  • The filing included information about ongoing investigations and cooperation with security firms to secure client environments.
Form 10-K:
  • The 10-K filing provided a broader overview of Snowflake's operations, financial performance, and risk factors.
  • It outlined the potential financial and reputational impacts of the breach, the importance of security measures, and strategies to prevent future incidents.By summarizing these documents and events, we see a comprehensive view of the Snowflake data breach, its causes, and the subsequent actions taken to mitigate its effects.Snowflake advised immediate implementation of MFA across all accounts.
  • Organizations were recommended to reset and rotate Snowflake credentials, and enforce network policy rules to restrict access to trusted locations only.
  • Snowflake provided Indicators of Compromise (IoCs) and collaborated with CrowdStrike and Mandiant for a thorough investigation​.

By summarizing these documents and events, we see a comprehensive view of the Snowflake data breach, its causes, and the subsequent actions taken to mitigate its effects.


Wednesday, 24 April 2024

Navigating the Cybersecurity Landscape: Harness Real-Time Exploit Detection and AI-Powered Solutions


🔍 The automated scanning tools that are available provide an easy list of vulnerabilities thereby gives an extensive list of exploits can be exploited. Keeping these exploit databases updated in real time can help organisations focus on the immediate mitigation areas to protect against any imminent threats that may occur due to the Known exploited Vulnerabilities.

These exploits will have clear attack path defined making the relevant application/system susceptible to successfully attack and compromise.

Exploitation involves attempting to use the identified vulnerabilities to gain unauthorised access to the target system. This can involve using various techniques, such as #Scanning #Enumerating #Fingerprinting #BruteforceAttacks #BufferOverflowAttacks #PrivilegeEscalation #SQLinjectionAttacks.

Exploit Frameworks like #Metasploit #CobaltStrike #Rootkit etc are most popularly used tools. The effort of finding best possible exploits comes from collated efforts of #SystemAdministrators #Researchers #Developers #PenTesters #EthicalHackers and sometimes #MaliciousHackers and these exploits are catalogued in databases like #Metasploit, #ExploitDatabase, #NISTNVD, #Rapid7




⚙️ To meet the evolving threat landscape of today's niche technologies, these listed advancements are much needed to be incorporated into security ecosystem.

- Update the vulnerabilities database in real time

- Update real time exploits

- Powering the exploit frameworks by AI capabilities

- Incorporating Prediction Model to analyse whether the vulnerability threat will be exploited or not

- Monitoring threat patterns and attack patterns with AI powered Solutions







Useful Links:

- CVE: https://cve.mitre.org/

- CVE Details: https://www.cvedetails.com/

- Metasploit: https://www.metasploit.com/

- Exploit Database: https://www.exploit-db.com/

- NIST NVD: https://nvd.nist.gov/vuln/

- Rapid7: https://www.rapid7.com/db/




🚀 My expertise in AI-driven threat intelligence has led to innovations like the Threat Modelling Tool, streamlining the efforts of cybersecurity specialists in mere minutes. Let's collaborate to fortify your security posture and stay ahead of emerging threats!

#Cybersecurity #AI #ThreatIntelligence #VulnerabilityManagement #ExploitDetection #PenTesting #Cybersecurity #Collaboration #Innovation #DefendAgainstThreats


Tuesday, 26 March 2024

Why is it significant to comply with the KEV (Known Exploited Vulnerabilities) Catalog?

In the realm of cybersecurity, staying ahead of threats is paramount. 

To aid this effort, the Cybersecurity and Infrastructure Security Agency (CISA) curates the Known Exploited Vulnerability (KEV) catalog, a pivotal resource for cybersecurity community & network defenders. This catalog compiles vulnerabilities that have been actively exploited, offering insights into immediate threats. It is imperative for organizations to prioritize remediation of these vulnerabilities to thwart potential compromises by threat actors.


All Federal Civilian Executive Branch (FCEB) agencies are mandated to address KEV catalog vulnerabilities under Binding Operational Directive (BOD) 22-01, all organizations, regardless of sector, can fortify their security posture by heeding these recommendations. Incorporating KEV catalog vulnerabilities into their vulnerability management plans fosters collective resilience across the cybersecurity posture of the organizations.

How to use the KEV Catalog:

Organizations should integrate the KEV catalog into their vulnerability management prioritization frameworks. This involves leveraging automated vulnerability and patch management tools that highlight or prioritize KEV vulnerabilities. 
The criteria for each of the three thresholds in updating the KEV Catalog are summarised as below:
  1. Assigned CVE ID: The process begins with the assignment of a Common Vulnerabilities and Exposures (CVE) ID. This unique identifier is issued by a CVE Numbering Authority (CNA) upon discovery of a cybersecurity vulnerability. MITRE Corporation oversees this process, with information published on the CVE and National Vulnerability Database (NVD) websites.
  2. Active Exploitation: A vulnerability's inclusion in the KEV catalog hinges on evidence of active exploitation in the wild. This entails unauthorized execution of malicious code by threat actors. Notably, attempted and successful exploitations are considered, while activities such as scanning or security research do not qualify.
  3. Clear Remediation Guidance: CISA adds vulnerabilities to the KEV catalog only when clear remediation actions are available. This typically involves applying updates per vendor instructions or, if necessary, removing affected products from networks. Mitigations may serve as temporary measures to prevent exploitation.
The KEV catalog serves as a beacon for organizations navigating the complex landscape of cybersecurity threats. By prioritizing remediation efforts based on actively exploited vulnerabilities, entities can bolster their defenses and contribute to a more resilient cybersecurity ecosystem. Collaborative efforts, informed decision-making and swift action are key in safeguarding against evolving threats in the digital age.

Friday, 22 March 2024

The 6th National Conference of "Innovative Global Technology Trends" by MIT-ADT Pune University - Cyber Security for AI

The 6th National Conference of "Innovative Global Technology Trends" by MIT-ADT Pune University.

At the conference, my guest lecture agenda encompassed the key topics of Cyber Security that were custom tailored for the needs of AI based technologies such as GenAI, LLM. Highlighted the approaches to Solution/Infrastructure Security, Data Security, AI Privacy, AI Risk & Threat Management. Also, an insightful discussion on AI frameworks, Compliance and live demonstration of AI-based threat analysis tool. 




Key Distinctions between AI solutions and traditional infrastructure are explored, alongside the need to redefine cybersecurity protocols for AI technologies. Addressing AI data security and Privacy, the lecture also delved into the concepts of Risk & Threat management, challenges of redefining the models and frameworks and need of the hour for defining policies, laws and regulations for the AI-driven world. Furthermore, the new architecture review techniques, baselining the controls, identifying new security domains as applicable in managing AI technology is scrutinised, prompting a reevaluation of current Cyber Security approach.

#Cybersecurity #AIPrivacy #AI #EUAIAct #GDPR #NIST #ThreatAnalysis #GenAI #LLM #Compliance #Policies #Frameworks

For further information on the topic of "Cyber Security for AI" and related discussions, feel free to reach out to me. Happy to collaborate for any training or consulting requirements.




Friday, 15 March 2024

Malware History and Evolution! - A brief history and evolution with AI

Malware History and Evolution! - A brief history and evolution with AI


For more than 60 years, computer viruses have been part of collective human consciousness, however what was once simply cyber vandalism has turned quickly to cybercrime. Worms, Trojans and viruses are evolving. Hackers are motivated and clever, always willing push the boundaries of connection and code to devise new infection methods. The future of cybercrime seems to involve more PoS (point of sale) hacks, and, perhaps, the recent Moker remote access Trojan is a good example of what's to come. This newly-discovered malware is hard to detect, difficult to remove and bypasses all known defenses. Nothing is certain—change is the lifeblood of both attack and defense.

AI powered malwares

To combat AI malware, security researchers and organizations are also leveraging artificial intelligence and machine learning techniques to develop advanced security solutions capable of detecting and mitigating AI-driven threats.


Evolution:


1950

Turing Test

Often considered the father of modern computer science, Alan Turing was famous for his work developing the first modern computers, decoding the encryption of German Enigma machines during the second world war, and detailing a procedure known as the Turing Test, forming the basis for artificial intelligence

1952 

The Checkers Program

The first AI program to run in the United States also was a checkers program, written in 1952 by Arthur Samuel for the prototype of the IBM 701.

1955

The Logic Theorist


December 1955 Herbert Simon and Allen Newell develop the Logic Theorist, the first artificial intelligence program, which eventually would prove 38 of the first 52 theorems in Whitehead and Russell's Principia Mathematica.

1956

Dartmouth College


The field of AI research was founded at a workshop held on the campus of Dartmouth College, USA during the summer of 1956.

John McCarthy coined the term "artificial intelligence" in 1956 and drove the development of the first AI programming language, LISP, in the 1960s. Early AI systems were rule-centric, which led to the development of more complex systems in the 1970s and 1980s, along with a boost in funding

Turing's theory suggests that with enough computational power and the right algorithms, we could create an AGI system that achieves parity with human intelligence. In other words, we could witness a profound convergence of human and machine capabilities, blurring the lines between what is human and what is artificial.

1966

Theory of Self-Replicating Automata

The paper was effectively a thought experiment that speculated that it would be possible for a "mechanical" organism—such as a piece of computer code—to damage machines, copy itself and infect new hosts, just like a biological virus

A self-replicating machine is a type of autonomous robot that is capable of reproducing itself autonomously using raw materials found in the environment, thus exhibiting self-replication in a way analogous to that found in nature.

1971

The Creeper Program

As noted by Discovery, the Creeper program, often regarded as the first virus, was created in 1971 by Bob Thomas of BBN. Creeper was actually designed as a security test to see if a self-replicating program was possible. It was—sort of. With each new hard drive infected, Creeper would try to remove itself from the previous host. Creeper had no malicious intent and only displayed a simple message: "I'M THE CREEPER. CATCH ME IF YOU CAN!"

1974

The Rabbit Virus

According to InfoCarnivore, the Rabbit (or Wabbit) virus was developed in 1974, did have malicious intent and was able to duplicate itself. Once on a computer, it made multiple copies of itself, severely reducing system performance and eventually crashing the machine. The speed of replication gave the virus its name.

1975

The First Trojan

Called ANIMAL, the first Trojan (although there is some debate as to whether this was a Trojan, or simply another virus) was developed by computer programmer John Walker in 1975, according to Fourmilab. At the time, "animal programs," which try to guess which animal the user is thinking of with a game of 20 questions, were extremely popular. The version Walker created was in high demand, and sending it to his friends meant making and transmitting magnetic tapes. To make things easier, Walker created PERVADE, which installed itself along with ANIMAL. While playing the game, PREVADE examined all computer directories available to the user and then made a copy of ANIMAL in any directories where it wasn't already present. There was no malicious intent here, but ANIMAL and PREVADE fit the definition of a Trojan: Hiding inside ANIMAL was another program that carried out actions without the user's approval

1982

Elk Cloner

Elk Cloner for the Apple II is developed. It spreads quickly across Apple II machines through floppy disks, and displays a short taunting poem.

1986

The Brain Boot Sector Virus

Brain, the first PC virus, began infecting 5.2" floppy disks in 1986. As Securelist reports, it was the work of two brothers, Basit and Amjad Farooq Alvi, who ran a computer store in Pakistan. Tired of customers making illegal copies of their software, they developed Brain, which replaced the boot sector of a floppy disk with a virus. The virus, which was also the first stealth virus, contained a hidden copyright message, but did not actually corrupt any data.

1986

Brain

The first computer virus for the IBM Personal Computer (IBM PC) was released on January 19, 1986, called "Brain". The virus was developed by Pakistani siblings Amjad and Basit Farooq Alvi to prevent copyright infringement by preventing users from using copied versions of their software.

1987 

The Jerusalem virus is released. 

Designed to destroy files on every occurrence of Friday the 13th, this is one of the first time-release viruses that have appeared repeatedly since

1992 

Michelangelo Worm

A media frenzy is created as the Michelangelo worm threatens to wipe machines around the world on March 6th. Damage is minimal, but the public profile of malware is raised

1999

Happy99 virus

The Melissa worm

Kak worm 

More advanced malware such as the Happy99 virus, the Melissa worm, and Kak worm are released. These spread very quickly through Microsoft environments used by many internet users

2000

The LoveLetter Virus

The introduction of reliable, speedy broadband networks early in the 21st century changed the way malware was transmitted. No longer confined to floppy disks or company networks, malware was now able to spread very quickly via email, via popular websites or even directly over the Internet. As a result, modern malware began to take shape. The threat landscape became a mixed environment shared by viruses, worms and Trojans—hence the name "malware" as an umbrella term for malicious software. One of the most serious epidemics of this new era was the LoveLetter, which appeared on May 4, 2000.


As Securelist notes, it followed the pattern of earlier email viruses of the time, but unlike the macro viruses that had dominated the threat landscape since 1995, it didn't take the form of an infected Word document, but arrived as a VBS file. It was simple and straightforward, and since users hadn't learned to be suspicious of unsolicited emails, it worked. The subject line was "I Love You," and each email contained an attachment, "LOVE-LETTER-FOR-YOU-TXT.vbs." The ILOVEYOU creator, Onel de Guzman, designed his worm to overwrite existing files and replace them with copies of itself, which were then used to spread the worm to all the victims' email contacts. Since the message often came to new victims from someone familiar, they were more likely to open it, making ILOVEYOU a proof-of-concept for the effectiveness of social engineering.

2000

Yahoo DDOS Attack

A 15-year-old Canadian boy crashes Yahoo.com via a DDoS attack. Yahoo was the number one search engine at the time

2001

The Code Red Virus

The Code Red computer worm was first observed on July 15, 2001, and infected more than 359,000 computers on July 19, 2001. It was the first malware to be classified as fileless, and it attacked computers running Microsoft's IIS web server. The worm is believed to have originated in Makati, Philippines. 

Code Red infected computers worldwide, particularly in Europe, North America, and Asia. The worm included the text string "Hacked by Chinese!" on web pages defaced by the malware. 

Code Red was discovered by eEye Digital Security employees Mark Maiffret and Ryan Permeh. They named it Code Red because they were drinking Code Red Mountain Dew at the time of the discovery. 

The Code Red worm was a "file less" worm—it existed only in memory and made no attempt to infect files on the system. Taking advantage of a flaw in the Microsoft Internet Information Server, the fast-replicating worm wreaked havoc by manipulating the protocols that allow computers to communicate and spread globally in just hours. Eventually, as noted in Scientific American, compromised machines were used to launch a distributed denial of service attack on the Whitehouse.gov website.

2001 

Nimda

Worms like Nimda are released, building off vulnerabilities and backdoor entrances created by earlier worms

2004

Santy

Santy, the first "webworm", spreads through phpBB and uses Google to find new targets.

2007 

Estonia DDoS Attack

Estonia is hit by a deliberate DDoS attack, crashing the prime minister's site as well as several government-run organizations such as schools and banks.

2008

Conficker

Conficker, one of the most widespread and notorious pieces of malware ever created, infects approximately 10 million Microsoft server systems, including government and military machines. The media attention garnered by Conficker helps further raise the idea of network security in the public consciousness.

2008 - 2009

Scareware

The number of "Scareware" programs - a program that looks like an anti-malware program but is in actuality a form of malware itself - rises rapidly. These programs continue to plague internet users with offers to scan their machines or remove supposedly serious viruses, while spreading their own malware when downloaded.

2010

Stuxnet

Stuxnet appears, and is alleged to have targeted Iranian nuclear facilities. It is widely viewed as the most advanced form of malware ever created.

Stuxnet is a well-known example of AI-powered malware. It was discovered in 2010 and specifically targeted industrial control systems, particularly those used in Iranian nuclear facilities. Stuxnet's AI capabilities allowed it to evade detection and spread efficiently by analyzing and exploiting vulnerabilities.

2012

Zappos

Zappos, a popular online ecommerce site specializing in shoes is hacked. During the security breach, the site's 24 million customers names, email addresses, partial credit card numbers and other information was exposed

2001

The Code Red Virus

The Code Red computer worm was first observed on July 15, 2001, and infected more than 359,000 computers on July 19, 2001. It was the first malware to be classified as fileless, and it attacked computers running Microsoft's IIS web server. The worm is believed to have originated in Makati, Philippines. 

Code Red infected computers worldwide, particularly in Europe, North America, and Asia. The worm included the text string "Hacked by Chinese!" on web pages defaced by the malware. 

Code Red was discovered by eEye Digital Security employees Mark Maiffret and Ryan Permeh. They named it Code Red because they were drinking Code Red Mountain Dew at the time of the discovery. 

The Code Red worm was a "file less" worm—it existed only in memory and made no attempt to infect files on the system. Taking advantage of a flaw in the Microsoft Internet Information Server, the fast-replicating worm wreaked havoc by manipulating the protocols that allow computers to communicate and spread globally in just hours. Eventually, as noted in Scientific American, compromised machines were used to launch a distributed denial of service attack on the Whitehouse.gov website.

2014

Heartbleed

One of the most recent of the major viruses came out in 2014, Heartbleed burst onto the scene and put servers across the Internet at risk. Heartbleed, unlike viruses or worms, stems from a vulnerability in OpenSSL, a general purpose, open source cryptographic library used by companies worldwide. OpenSSL periodically sends out "heartbeats" to ensure that secure endpoints are still connected. Users can send OpenSSL a specific amount of data and then ask for the same amount back—for example, one byte. If users claim they're sending the maximum allowed, 64 kilobytes, but only send a single byte, the server will respond with the last 64 kilobytes of data stored in RAM, notes security technologist, Bruce Schneier, which could include anything from user names to passwords to secure encryption keys.

2018

Deeplocker

DeepLocker is an AI-powered malware developed by IBM Security. It uses AI techniques, specifically deep learning algorithms, to target specific victims and remain undetected until specific conditions are met. DeepLocker's AI capabilities make it highly sophisticated and difficult to detect by traditional antivirus systems.


“What is unique about DeepLocker is that the use of AI makes the 'trigger conditions' to unlock the attack almost impossible to reverse engineer. The malicious payload will only be unlocked if the intended target is reached. It achieves this by using a deep neural network (DNN) AI model,” Stoecklin writes.

2017

Mylobot

Mylobot is a complex botnet malware that uses AI techniques to evade detection and maintain persistence on infected systems. It employs machine learning algorithms to analyze the system's behaviour and adapt its attack accordingly, making it highly resilient and persistent.


Mylobot is a malware that first appeared in 2017, and has been used to infect Windows systems for over two years. It was discovered and named by Deep Instinct in 2018. Mylobot has three stages:

First stage: Embeds an encrypted resource and performs anti-debug checks

Second stage: Contains two resources: an encrypted resource and a small RC4 key

Third stage: Turns the infected computer into a proxy 

2016

Mirai

Mirai is an infamous malware that targeted Internet of Things (IoT) devices, such as routers and cameras. While not strictly AI-based, Mirai used machine learning techniques to identify and infect vulnerable IoT devices, creating a massive botnet that was later used to launch DDoS attacks.


The Mirai malware was first used in September 2016, when the authors launched a DDoS attack on the website of a security expert. The malware was named after the anime series Mirai Nikki and was developed by Paras, Josiah, and Dalton, who finished the first version in August 2016. The malware infected vulnerable devices like smart cameras, DVRs, and routers, and scanned the internet for targets by trying default username and password combinations.


In September 2017, Anna-senpai, who some believe is the author of Mirai, released the source code to a hacking forum. Other cybercriminals quickly replicated the code.

2014

Emotet

Emotet is a sophisticated banking trojan that has continually evolved its capabilities. While not primarily based on AI, it has shown AI-like behaviour, such as self-propagation and bypassing security measures by learning from its environment. Emotet has been highly successful in infecting systems globally and spreading other malware payloads.


Emotet was first discovered in 2014 by security researchers who were tracking a malicious network traffic pattern. It was quickly identified as a Trojan virus that could gain access to computers through email attachments or malicious links sent via email campaigns or social media messages.

It's a modular banking trojan that can gain access to computers through email attachments or malicious links sent via email campaigns or social media messages. It's also known as Heodo and Geodo



Emotet has evolved into the go-to solution for cybercriminals over the years. It's operated by a cybercrime group known as Mealybug or TA542. Emotet spreads through spam emails (Malspam) via infected attachments and embedded malicious URLs. Once Emotet has access to a network, it can spread by cracking passwords to accounts using the brute force method. 

Emotet has had notable infections in the following years:

2018: Allentown, Pennsylvania

2019: Heise Online, Kammergericht Berlin, and Humboldt University of Berlin

2020: Department of Justice of the province of Quebec and Lithuanian government 

Security researchers and companies released small indications of Emotet's activity on social media from late 2021 to late 2022.

2021

WormGPT

Emergence of malicious AI toolkits, which are AI large language models (LLMs)


WormGPT first came into existence in March 2021, and the creator began offering access to the platform on a hacker forum in June 2021. FraudGPT is a tool that cybercriminals use to create undetectable malware and malicious content. The tool is built on ChatGPT-3 technology and can produce coherent texts based on user prompts. FraudGPT can:

Create undetectable viruses or malware

Generate phishing pages and hacking tools

Find non-VBV bins

Craft scam pages or letters

Uncover leaks, vulnerabilities and access active cards

2023

FraudGPT

FraudGPT: The dark evolution of ChatGPT into an AI weapon for cybercriminals in 2023 | Data Science Dojo


FraudGPT has been circulating in darknet forums and Telegram channels since July 22, 2023, and is available through subscription at a cost of $200 per month, $1,000 for six months, or $1,700 for a year.


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