♂️ Github repositories every Developer should know ♂️
❗️A very popular repo that curates all topics from Development, testing, business, etc, etc.
https://github.com/sindresorhus/awesome
❗️Clean Code JavaScript
https://github.com/ryanmcdermott/clean-code-javascript
❗️Tech Interview Handbook
https://github.com/yangshun/tech-interview-handbook
❗️Developer Roadmaps, articles and resources for developers.
https://github.com/kamranahmedse/developer-roadmap
❗️33 JS Concepts
https://github.com/leonardomso/33-js-concepts
❗️Best websites a programmer should visit
https://github.com/sdmg15/Best-websites-a-programmer-should-visit
❗️Design Resources for Developers
https://github.com/bradtraversy/design-resources-for-developers
❗️Big List of Naughty Strings:
https://github.com/minimaxir/big-list-of-naughty-strings
❗️A very popular repo that curates all topics from Development, testing, business, etc, etc.
https://github.com/sindresorhus/awesome
❗️Clean Code JavaScript
https://github.com/ryanmcdermott/clean-code-javascript
❗️Tech Interview Handbook
https://github.com/yangshun/tech-interview-handbook
❗️Developer Roadmaps, articles and resources for developers.
https://github.com/kamranahmedse/developer-roadmap
❗️33 JS Concepts
https://github.com/leonardomso/33-js-concepts
❗️Best websites a programmer should visit
https://github.com/sdmg15/Best-websites-a-programmer-should-visit
❗️Design Resources for Developers
https://github.com/bradtraversy/design-resources-for-developers
❗️Big List of Naughty Strings:
https://github.com/minimaxir/big-list-of-naughty-strings
GitHub
GitHub - sindresorhus/awesome: 😎 Awesome lists about all kinds of interesting topics
😎 Awesome lists about all kinds of interesting topics - sindresorhus/awesome
The repository includes links to various tools, frameworks, and resources that can be used by Red Teamers to conduct attacks, as well as resources for defenders to improve their security posture.
The repository is organized into several categories, including reconnaissance and information gathering, exploitation, post-exploitation, and defense evasion. Each category contains links to various tools and resources that can be used in Red Team Operations.
Some of the tools and frameworks included in the repository are well-known and widely used, such as Metasploit, Cobalt Strike, and Empire. Other tools and frameworks are less well-known but still valuable for Red Team Operations, such as BloodHound, which is used for Active Directory reconnaissance, and GoPhish, which is used for phishing simulations.
In addition to tools and frameworks, the repository also includes links to blogs, articles, and other resources that provide guidance on conducting Red Team Operations and improving overall security posture. These resources cover a wide range of topics, including social engineering, network infrastructure, and application security.
Overall, the "Awesome-Red-Team-Operations" repository is a valuable resource for Red Teamers, defenders, and anyone interested in learning more about cybersecurity.
https://github.com/CyberSecurityUP/Awesome-Red-Team-Operations
The repository is organized into several categories, including reconnaissance and information gathering, exploitation, post-exploitation, and defense evasion. Each category contains links to various tools and resources that can be used in Red Team Operations.
Some of the tools and frameworks included in the repository are well-known and widely used, such as Metasploit, Cobalt Strike, and Empire. Other tools and frameworks are less well-known but still valuable for Red Team Operations, such as BloodHound, which is used for Active Directory reconnaissance, and GoPhish, which is used for phishing simulations.
In addition to tools and frameworks, the repository also includes links to blogs, articles, and other resources that provide guidance on conducting Red Team Operations and improving overall security posture. These resources cover a wide range of topics, including social engineering, network infrastructure, and application security.
Overall, the "Awesome-Red-Team-Operations" repository is a valuable resource for Red Teamers, defenders, and anyone interested in learning more about cybersecurity.
https://github.com/CyberSecurityUP/Awesome-Red-Team-Operations
GitHub
GitHub - CyberSecurityUP/Awesome-Red-Team-Operations
Contribute to CyberSecurityUP/Awesome-Red-Team-Operations development by creating an account on GitHub.
Forwarded from RED TEAM COLLECTIONS
Artificial Intelligence (
As we move towards the future, the importance of AI is only going to increase. The growth of AI has been exponential, especially in the fields of development and cybersecurity. There are new tools, techniques, and innovations being introduced every day, and it's important to keep up with the latest trends.
That's why we're excited to announce that we're hosting a discussion on the
topic "AI the Future" today from 9️⃣ PM to 1️⃣0️⃣ PM IST. During the discussion, we will be exploring different aspects of AI and its impact on the world. We invite everyone to participate, share their ideas, and discuss the latest tools, topics, and innovations related to AI.We hope that this discussion will be an opportunity for all of us to learn and explore something new. It's a chance to expand our knowledge and to stay up to date with the latest trends in the world of AI.
Don't forget to subscribe to us on Instagram to stay updated with our latest events and discussions.
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Info sec |•| Red Team
In this group we will discuss about cyber sec
Our channel or group is not responsible for any type illegal activities
Our channel or group is not responsible for any type illegal activities
To ensure the security of this channel, the release of the Offsec Leak will be postponed to another channel.
https://t.me/+RaY74LwsK14wZTBl
You will receive the link to access the leak in that channel. We will leak the information on that channel, but please note that the link will expire in 24 hours.
https://t.me/+RaY74LwsK14wZTBl
You will receive the link to access the leak in that channel. We will leak the information on that channel, but please note that the link will expire in 24 hours.
As manny people ask for PEN 200
My team will make a study Guide in a way to learn it better way
Note: It is not a from Offsec we make this for your practice purpose and it will be posted today evening
if you like what we did or you have any suggestion's regarding this topic you can send your thought on @Cyberscurity_bot this bot we will try to implement in any way possible
Hope you like innovative way of learning
My team will make a study Guide in a way to learn it better way
Note: It is not a from Offsec we make this for your practice purpose and it will be posted today evening
if you like what we did or you have any suggestion's regarding this topic you can send your thought on @Cyberscurity_bot this bot we will try to implement in any way possible
Hope you like innovative way of learning
list of tools and resources related to the use of machine learning for cybersecurity:
Datasets:
UNM Cybersecurity Data Repository: A collection of network traffic datasets for cybersecurity research.
DARPA Cyber Grand Challenge Dataset: A dataset containing network traffic and other data from the DARPA Cyber Grand Challenge.
NSL-KDD Dataset: A dataset for network intrusion detection research.
Papers:
"A survey of machine learning for big data processing in cybersecurity": A survey paper on the use of machine learning for big data processing in cybersecurity.
"Machine Learning Techniques in Cybersecurity": A review of various machine learning techniques used in cybersecurity.
"Deep Learning for Network Intrusion Detection: A Survey": A survey paper on the use of deep learning for network intrusion detection.
Books:
"Applied Machine Learning for Cyber Security": A book that covers the application of machine learning techniques to various cybersecurity problems.
"Machine Learning and Security: Protecting Systems with Data and Algorithms": A book that covers the use of machine learning in cybersecurity from both offensive and defensive perspectives.
Talks:
"Applying Machine Learning to Cybersecurity": A talk by David Bianco on the application of machine learning to cybersecurity.
"Machine Learning and Cyber Security": A talk by Sven Krasser on the use of machine learning in cybersecurity.
"Using Machine Learning to Detect Malicious Activity": A talk by Jamie Butler on the use of machine learning to detect malicious activity.
Tutorials:
"Machine Learning for Cybersecurity Course": A free online course on the use of machine learning in cybersecurity.
"A Gentle Introduction to Machine Learning in Cybersecurity": A tutorial on the basics of machine learning in cybersecurity.
"Building a Machine Learning Model for Network Intrusion Detection": A tutorial on building a machine learning model for network intrusion detection.
Courses:
"Applied Data Science with Python Specialization": A Coursera specialization that includes a course on Applied Machine Learning in Python, which covers the application of machine learning to cybersecurity.
"Machine Learning for Cybersecurity Professionals": A SANS Institute course on the use of machine learning in cybersecurity.
Miscellaneous:
"Awesome Machine Learning for Cyber Security": A curated list of resources related to the use of machine learning in cybersecurity.
"OpenAI Cybersecurity Initiative": A research initiative focused on the application of machine learning to cybersecurity problems.
Datasets:
UNM Cybersecurity Data Repository: A collection of network traffic datasets for cybersecurity research.
DARPA Cyber Grand Challenge Dataset: A dataset containing network traffic and other data from the DARPA Cyber Grand Challenge.
NSL-KDD Dataset: A dataset for network intrusion detection research.
Papers:
"A survey of machine learning for big data processing in cybersecurity": A survey paper on the use of machine learning for big data processing in cybersecurity.
"Machine Learning Techniques in Cybersecurity": A review of various machine learning techniques used in cybersecurity.
"Deep Learning for Network Intrusion Detection: A Survey": A survey paper on the use of deep learning for network intrusion detection.
Books:
"Applied Machine Learning for Cyber Security": A book that covers the application of machine learning techniques to various cybersecurity problems.
"Machine Learning and Security: Protecting Systems with Data and Algorithms": A book that covers the use of machine learning in cybersecurity from both offensive and defensive perspectives.
Talks:
"Applying Machine Learning to Cybersecurity": A talk by David Bianco on the application of machine learning to cybersecurity.
"Machine Learning and Cyber Security": A talk by Sven Krasser on the use of machine learning in cybersecurity.
"Using Machine Learning to Detect Malicious Activity": A talk by Jamie Butler on the use of machine learning to detect malicious activity.
Tutorials:
"Machine Learning for Cybersecurity Course": A free online course on the use of machine learning in cybersecurity.
"A Gentle Introduction to Machine Learning in Cybersecurity": A tutorial on the basics of machine learning in cybersecurity.
"Building a Machine Learning Model for Network Intrusion Detection": A tutorial on building a machine learning model for network intrusion detection.
Courses:
"Applied Data Science with Python Specialization": A Coursera specialization that includes a course on Applied Machine Learning in Python, which covers the application of machine learning to cybersecurity.
"Machine Learning for Cybersecurity Professionals": A SANS Institute course on the use of machine learning in cybersecurity.
Miscellaneous:
"Awesome Machine Learning for Cyber Security": A curated list of resources related to the use of machine learning in cybersecurity.
"OpenAI Cybersecurity Initiative": A research initiative focused on the application of machine learning to cybersecurity problems.
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GDB HackerOne
DATASETS
HIKARI-2021 Datasets: A collection of datasets for malware classification, phishing detection, and intrusion detection.
Samples of Security Related Data: A collection of datasets for network traffic analysis, intrusion detection, and malware analysis.…
HIKARI-2021 Datasets: A collection of datasets for malware classification, phishing detection, and intrusion detection.
Samples of Security Related Data: A collection of datasets for network traffic analysis, intrusion detection, and malware analysis.…
DATASETS
HIKARI-2021 Datasets: A collection of datasets for malware classification, phishing detection, and intrusion detection.
Samples of Security Related Data: A collection of datasets for network traffic analysis, intrusion detection, and malware analysis.
DARPA Intrusion Detection Data Sets [ 1998 / 1999 ]: A set of datasets created for the DARPA intrusion detection evaluation program.
Stratosphere IPS Data Sets: A collection of datasets for intrusion detection, botnet detection, and malware analysis.
Open Data Sets: A list of open data sets for various cybersecurity applications, including botnet detection, phishing detection, and malware analysis.
Data Capture from National Security Agency: A collection of datasets created by the National Security Agency (NSA) for network traffic analysis.
The ADFA Intrusion Detection Data Sets: A set of datasets created for the Australian Defence Force Academy (ADFA) intrusion detection evaluation program.
NSL-KDD Data Sets: A set of datasets for intrusion detection research.
Malicious URLs Data Sets: A collection of datasets for malicious URL detection.
Multi-Source Cyber-Security Events: A collection of datasets for cybersecurity event analysis.
KDD Cup 1999 Data: A dataset for intrusion detection research.
Web Attack Payloads: A collection of payloads for web attack simulation.
WAF Malicious Queries Data Sets: A collection of datasets for web application firewall testing.
Malware Training Data Sets: A collection of datasets for malware analysis and classification.
Aktaion Data Sets: A collection of datasets for intrusion detection and malware analysis.
CRIME Database from DeepEnd Research: A database of malware samples for research purposes.
Publicly available PCAP files: A collection of network traffic capture files for various cybersecurity applications.
2007 TREC Public Spam Corpus: A dataset for spam detection research.
Drebin Android Malware Dataset: A dataset for Android malware analysis.
PhishingCorpus Dataset: A dataset for phishing detection research.
EMBER: A dataset for detecting malware in executables.
Vizsec Research: A collection of datasets for cybersecurity visualization research.
SHERLOCK: A dataset for identifying malicious domains.
Probing / Port Scan - Dataset: A dataset for network probing and port scanning analysis.
Aegean Wireless Intrusion Dataset (AWID): A dataset for wireless network intrusion detection.
BODMAS PE Malware Dataset: A dataset for malware analysis and classification.
HIKARI-2021 Datasets: A collection of datasets for malware classification, phishing detection, and intrusion detection.
Samples of Security Related Data: A collection of datasets for network traffic analysis, intrusion detection, and malware analysis.
DARPA Intrusion Detection Data Sets [ 1998 / 1999 ]: A set of datasets created for the DARPA intrusion detection evaluation program.
Stratosphere IPS Data Sets: A collection of datasets for intrusion detection, botnet detection, and malware analysis.
Open Data Sets: A list of open data sets for various cybersecurity applications, including botnet detection, phishing detection, and malware analysis.
Data Capture from National Security Agency: A collection of datasets created by the National Security Agency (NSA) for network traffic analysis.
The ADFA Intrusion Detection Data Sets: A set of datasets created for the Australian Defence Force Academy (ADFA) intrusion detection evaluation program.
NSL-KDD Data Sets: A set of datasets for intrusion detection research.
Malicious URLs Data Sets: A collection of datasets for malicious URL detection.
Multi-Source Cyber-Security Events: A collection of datasets for cybersecurity event analysis.
KDD Cup 1999 Data: A dataset for intrusion detection research.
Web Attack Payloads: A collection of payloads for web attack simulation.
WAF Malicious Queries Data Sets: A collection of datasets for web application firewall testing.
Malware Training Data Sets: A collection of datasets for malware analysis and classification.
Aktaion Data Sets: A collection of datasets for intrusion detection and malware analysis.
CRIME Database from DeepEnd Research: A database of malware samples for research purposes.
Publicly available PCAP files: A collection of network traffic capture files for various cybersecurity applications.
2007 TREC Public Spam Corpus: A dataset for spam detection research.
Drebin Android Malware Dataset: A dataset for Android malware analysis.
PhishingCorpus Dataset: A dataset for phishing detection research.
EMBER: A dataset for detecting malware in executables.
Vizsec Research: A collection of datasets for cybersecurity visualization research.
SHERLOCK: A dataset for identifying malicious domains.
Probing / Port Scan - Dataset: A dataset for network probing and port scanning analysis.
Aegean Wireless Intrusion Dataset (AWID): A dataset for wireless network intrusion detection.
BODMAS PE Malware Dataset: A dataset for malware analysis and classification.
DNS stands for Domain Name System:
- DNS translates human-readable domain names into IP addresses for computers to communicate over the internet
- DNS requests are sent to a configured DNS resolver if not found in cache
How DNS Works:
- DNS resolver checks for associated DNS record in cache or sends request to root server for TLD server's IP address
- TLD server responds with authoritative server's IP address for domain, which sends back IP address of domain
- Common DNS record types include NS, A, MX, PTR, CNAME, and TXT
DNS Zone Transfer:
- DNS zone transfer is the process of transferring a copy of the DNS zone file from primary to secondary DNS server
- Zone transfer is needed due to the critical nature of DNS and need for redundancy
- AXFR is a client-initiated request used for DNS zone transfer
DNS Enumeration Using Zone Transfer:
- DNS enumeration using zone transfer involves retrieving entire zone file for a domain from DNS server
- Techniques include using dig command in Linux or nslookup command in Windows
- Tools such as DNSRecon, DNSEnum, and Nmap broadcast-dns-service-discovery script can also be used
- Prevention includes not allowing untrusted hosts to transfer zones and ensuring private hostnames are not referenced to IP addresses
DNS Cache Poisoning (DNS Spoofing):
- DNS cache poisoning involves entering false information into a DNS cache
DNS resolvers save responses to IP address queries for a certain amount of time
- Attackers can poison DNS caches by impersonating DNS nameservers and forging replies when DNS resolver queries nameserver
- DNS uses both UDP and TCP for communication between clients and servers
What are DNS poisoning attacks?:
- DNS poisoning attacks exploit vulnerabilities in the DNS system to inject false information into the cache of a DNS server.
- Attackers can accomplish this through various means, such as exploiting weaknesses in the DNS server software or intercepting and modifying DNS queries and responses in transit.
Why are DNS poisoning attacks a concern?:
- DNS poisoning attacks can be difficult to carry out due to the short amount of time attackers have to send a forged response before the real response arrives.
- However, attackers can still carry out DNS poisoning attacks if they know or guess certain factors, such as which DNS queries are not cached by the targeted DNS resolver.
How can DNS poisoning be prevented?:
- DNSSEC (Domain Name System Security Extensions) can be used to verify DNS data integrity and origin.
- DNSSEC uses public key cryptography to sign and verify DNS responses, ensuring that they have not been tampered with.
What is a flaw in DNSSEC?:
- NSEC record types in DNSSEC contain cryptography information and the name of the closest existing domain name in a zone, along with the name of the next domain name that would exist in the zone if it did exist.
- Attackers can use this information to enumerate all secret subdomains if DNSSEC is not properly configured.
How can DNSSEC zone walking be prevented?:
- NSEC3 records can be used instead of NSEC records to contain salted hash values of non-existent domain names and closest existing domain names in a zone.
- This prevents attackers from understanding the subdomains as the values are hashed.
What is DNS cache snooping?:
- DNS cache snooping is a type of attack where an attacker tries to obtain information about the DNS queries and responses made by a target user or network.
- The attacker does this by analyzing the contents of the DNS cache maintained by the target’s DNS resolver.
How can DNS cache snooping be avoided?:
- Disabling non-recursive queries can prevent DNS cache snooping by ensuring that all queries are performed recursively and IP addresses are generated by the DNS hierarchy of servers.
What are some tools used for DNSSEC zone walking?:
- LDNS and DNSRecon are common tools used for DNSSEC zone walking.
- Nmap script dns-nsec-snum can also be used for DNSSEC zone walking.
- DNS translates human-readable domain names into IP addresses for computers to communicate over the internet
- DNS requests are sent to a configured DNS resolver if not found in cache
How DNS Works:
- DNS resolver checks for associated DNS record in cache or sends request to root server for TLD server's IP address
- TLD server responds with authoritative server's IP address for domain, which sends back IP address of domain
- Common DNS record types include NS, A, MX, PTR, CNAME, and TXT
DNS Zone Transfer:
- DNS zone transfer is the process of transferring a copy of the DNS zone file from primary to secondary DNS server
- Zone transfer is needed due to the critical nature of DNS and need for redundancy
- AXFR is a client-initiated request used for DNS zone transfer
DNS Enumeration Using Zone Transfer:
- DNS enumeration using zone transfer involves retrieving entire zone file for a domain from DNS server
- Techniques include using dig command in Linux or nslookup command in Windows
- Tools such as DNSRecon, DNSEnum, and Nmap broadcast-dns-service-discovery script can also be used
- Prevention includes not allowing untrusted hosts to transfer zones and ensuring private hostnames are not referenced to IP addresses
DNS Cache Poisoning (DNS Spoofing):
- DNS cache poisoning involves entering false information into a DNS cache
DNS resolvers save responses to IP address queries for a certain amount of time
- Attackers can poison DNS caches by impersonating DNS nameservers and forging replies when DNS resolver queries nameserver
- DNS uses both UDP and TCP for communication between clients and servers
What are DNS poisoning attacks?:
- DNS poisoning attacks exploit vulnerabilities in the DNS system to inject false information into the cache of a DNS server.
- Attackers can accomplish this through various means, such as exploiting weaknesses in the DNS server software or intercepting and modifying DNS queries and responses in transit.
Why are DNS poisoning attacks a concern?:
- DNS poisoning attacks can be difficult to carry out due to the short amount of time attackers have to send a forged response before the real response arrives.
- However, attackers can still carry out DNS poisoning attacks if they know or guess certain factors, such as which DNS queries are not cached by the targeted DNS resolver.
How can DNS poisoning be prevented?:
- DNSSEC (Domain Name System Security Extensions) can be used to verify DNS data integrity and origin.
- DNSSEC uses public key cryptography to sign and verify DNS responses, ensuring that they have not been tampered with.
What is a flaw in DNSSEC?:
- NSEC record types in DNSSEC contain cryptography information and the name of the closest existing domain name in a zone, along with the name of the next domain name that would exist in the zone if it did exist.
- Attackers can use this information to enumerate all secret subdomains if DNSSEC is not properly configured.
How can DNSSEC zone walking be prevented?:
- NSEC3 records can be used instead of NSEC records to contain salted hash values of non-existent domain names and closest existing domain names in a zone.
- This prevents attackers from understanding the subdomains as the values are hashed.
What is DNS cache snooping?:
- DNS cache snooping is a type of attack where an attacker tries to obtain information about the DNS queries and responses made by a target user or network.
- The attacker does this by analyzing the contents of the DNS cache maintained by the target’s DNS resolver.
How can DNS cache snooping be avoided?:
- Disabling non-recursive queries can prevent DNS cache snooping by ensuring that all queries are performed recursively and IP addresses are generated by the DNS hierarchy of servers.
What are some tools used for DNSSEC zone walking?:
- LDNS and DNSRecon are common tools used for DNSSEC zone walking.
- Nmap script dns-nsec-snum can also be used for DNSSEC zone walking.
DNS Cache Poisoning:
- DNS cache poisoning is a type of attack in which an attacker exploits vulnerabilities in DNS servers to insert fake DNS records into their cache.
- This can allow the attacker to redirect users to malicious websites, intercept sensitive data, or perform other nefarious actions.
DNS Zone Transfers:
- DNS zone transfers can be used by attackers to gather information about a target organization's DNS infrastructure.
- It is important for organizations to properly configure their DNS servers to prevent unauthorized zone transfers.
DNS Cache Snooping:
- DNS cache snooping is a technique used by attackers to determine whether a DNS resolver has cached records for a particular domain.
- This can be used to identify vulnerable DNS servers and launch more targeted attacks.
Preventing DNS Cache Snooping:
- Preventive measures to prevent DNS cache snooping include not having externally accessible DNS servers, not allowing public access to DNS servers that require recursion, and using DNS rate limiting.
- It is important for organizations to properly configure their DNS servers to prevent these types of attacks.
- DNS cache poisoning is a type of attack in which an attacker exploits vulnerabilities in DNS servers to insert fake DNS records into their cache.
- This can allow the attacker to redirect users to malicious websites, intercept sensitive data, or perform other nefarious actions.
DNS Zone Transfers:
- DNS zone transfers can be used by attackers to gather information about a target organization's DNS infrastructure.
- It is important for organizations to properly configure their DNS servers to prevent unauthorized zone transfers.
DNS Cache Snooping:
- DNS cache snooping is a technique used by attackers to determine whether a DNS resolver has cached records for a particular domain.
- This can be used to identify vulnerable DNS servers and launch more targeted attacks.
Preventing DNS Cache Snooping:
- Preventive measures to prevent DNS cache snooping include not having externally accessible DNS servers, not allowing public access to DNS servers that require recursion, and using DNS rate limiting.
- It is important for organizations to properly configure their DNS servers to prevent these types of attacks.
❤1
Forwarded from Microsoft Developer
The Microsoft Learn Cloud Skills #Challenge
There are 8 challenges available to choose from, select one that's right for you. Once you complete that challenge you will earn a free Microsoft Certification exam that can be applied to your choice from a select list of options.
Free Microsoft Certification exam offer will be delivered by June 30, 2023, and will expire on September 27, 2023. You must complete your exam before this date. There are 8 challenges available to choose from, select one that's right for you. Once you complete that challenge you will earn a free Microsoft Certification exam that can be applied to your choice from a select list of options.
AWS DeepRacer Student
Learn machine learning, win prizes by racing with students globally, and complete your application to the AWS AI & ML Scholarship program
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OpenAI, the developer of ChatGPT, has chosen London as the location for its first international office in a boost to the UK’s attempts to stay competitive in the artificial intelligence race.
The San Francisco-based company behind the popular chatbot said on Wednesday that it would start its expansion outside the US in the UK capital.
OpenAI said the UK office would reinforce efforts to create “safe AGI”. AGI refers to artificial general intelligence, or a highly intelligent AI system that OpenAI’s chief executive, Sam Altman, has described as “generally smarter than humans”.
The San Francisco-based company behind the popular chatbot said on Wednesday that it would start its expansion outside the US in the UK capital.
OpenAI said the UK office would reinforce efforts to create “safe AGI”. AGI refers to artificial general intelligence, or a highly intelligent AI system that OpenAI’s chief executive, Sam Altman, has described as “generally smarter than humans”.
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