READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Chatbots could be used to steal data, says cybersecurity agency

The UK’s cybersecurity agency has warned that there is an increasing risk that chatbots could be manipulated by hackers.

The National Cyber Security Centre (NCSC) has said that individuals could manipulate the prompts of chatbots, which run on artificial intelligence by creating a language model and give answers to questions by users, through “prompt injection” attacks that would make them behave in an unintended manner.

The point of a chatbot is to mimic human-like conversations, which it has been trained to do through scraping large amounts of data. Commonly used in online banking or online shopping, chatbots are generally designed to handle simple requests.

Large language models, such as OpenAI’s ChatGPT and Google’s AI chatbot Bard, are trained using data that generates human-like responses to user prompts. Since chatbots are used to pass data to third-party applications and services, the NCSC has said that risks from malicious “prompt injection” will grow.

For instance, if a user inputs a statement or question that a language model is not familiar with, or if they find a combination of words to override the model’s original script or prompts, the user can cause the model to perform unintended actions.

Such inputs could cause a chatbot to generate offensive content or reveal confidential information in a system that accepts unchecked input.

According to the NCSC, prompt injection attacks can also cause real world consequences, if systems are not designed with security. The vulnerability of chatbots and the ease with which prompts can be manipulated could cause attacks, scams and data theft. The large language models are increasingly used to pass data to third-party applications and services, meaning the risks from malicious prompt injection will grow.

The NCSC said: “Prompt injection and data poisoning attacks can be extremely difficult to detect and mitigate. However, no model exists in isolation, so what we can do is design the whole system with security in mind.”

The NCSC said that cyber-attacks caused by artificial intelligence and machine learning that leaves systems vulnerable can be mitigated through designing for security and understanding the attack techniques that exploit “inherent vulnerabilities” in machine learning algorithm.

Adapted from: The Guardian, Wednesday 30 August 2023, page 4.

The newspaper headline expresses the agency’s:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Chatbots could be used to steal data, says cybersecurity agency

The UK’s cybersecurity agency has warned that there is an increasing risk that chatbots could be manipulated by hackers.

The National Cyber Security Centre (NCSC) has said that individuals could manipulate the prompts of chatbots, which run on artificial intelligence by creating a language model and give answers to questions by users, through “prompt injection” attacks that would make them behave in an unintended manner.

The point of a chatbot is to mimic human-like conversations, which it has been trained to do through scraping large amounts of data. Commonly used in online banking or online shopping, chatbots are generally designed to handle simple requests.

Large language models, such as OpenAI’s ChatGPT and Google’s AI chatbot Bard, are trained using data that generates human-like responses to user prompts. Since chatbots are used to pass data to third-party applications and services, the NCSC has said that risks from malicious “prompt injection” will grow.

For instance, if a user inputs a statement or question that a language model is not familiar with, or if they find a combination of words to override the model’s original script or prompts, the user can cause the model to perform unintended actions.

Such inputs could cause a chatbot to generate offensive content or reveal confidential information in a system that accepts unchecked input.

According to the NCSC, prompt injection attacks can also cause real world consequences, if systems are not designed with security. The vulnerability of chatbots and the ease with which prompts can be manipulated could cause attacks, scams and data theft. The large language models are increasingly used to pass data to third-party applications and services, meaning the risks from malicious prompt injection will grow.

The NCSC said: “Prompt injection and data poisoning attacks can be extremely difficult to detect and mitigate. However, no model exists in isolation, so what we can do is design the whole system with security in mind.”

The NCSC said that cyber-attacks caused by artificial intelligence and machine learning that leaves systems vulnerable can be mitigated through designing for security and understanding the attack techniques that exploit “inherent vulnerabilities” in machine learning algorithm.

Adapted from: The Guardian, Wednesday 30 August 2023, page 4.

Based on the text, mark the statements below as true (T) or false (F).

( ) Chatbots have been trained to emulate human communication.

( ) Problems in cybersecurity have ceased to exist.

( ) Control over confidential data is still at risk.

The statements are, respectively:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

The aim of the last paragraph is to:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

The word “downsides” in “There are downsides to both categories” (7^th paragraph) means:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

In the question “Or create laws or a code of conduct that probably would be ignored?” (5^th paragraph), the authors imply that these laws and code of conduct may be:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

When the authors refer to the use of deepfake in education (2^nd paragraph), they state that ultimately teachers may find it:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

In the 1^st sentence (“It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape”), the reaction of society is described as being one of:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

Based on the text, mark the statements below as true (T) or false (F).

( ) Deepfakes are circumscribed to certain areas of action.

( ) The sole aim of deepfake technology is to spread misinformation.

( ) Evidence shows that even high-ranking executives can be easy targets to vishing techniques.

The statements are, respectively:

Uma lista com 2023 números inteiros positivos tem moda única que ocorre exatamente 23 vezes.

A quantidade mínima de números inteiros diferentes nessa lista é:

Um cilindro e um cone têm o mesmo volume. O cilindro tem 60 cm de altura e o raio da base do cone é o dobro do raio da base do cilindro.

A altura do cone mede: