Introduction
Since the coinage of the term in 1956, Artificial Intelligence (AI) has evolved considerably. From its metaphorical reference in Mary Shelly’s Frankenstein, to its most popular recent application in autonomous cars, AI has made a progressive shift, over the years. It influences all the major industries such as transportation, communication, banking, education, healthcare, media, etc.
When it comes to cybersecurity, AI is changing how we detect and respond to threats. However, with the benefits, comes the risk of the potential misuse of AI capabilities. Is the primary catalyst for cybersecurity, also a threat to it?
How do we use AI in our daily life?
Social media users encounter AI on a daily basis and probably don’t recognize it at all. Online shopping recommendations, image recognition, personal assistants such as Siri and Alexa, and smart email replies, are the most popular examples.
For instance, Facebook identifies individual faces in a photo, and helps users “tag” and notify them. Businesses often embed chatbots in their websites and applications. These AI-driven chatbots detect words in the questions entered by customers, to predict and deliver prompt responses.
How do malicious actors abuse and weaponize AI?
To orchestrate attacks, cyber criminals often tinker with existing AI systems, instead of developing new AI programs and tools. Some common attacks that exploit Artificial Intelligence include:
- Misusing the nature of AI algorithms/ systems: AI capabilities such as efficiency, speed and accuracy can be used to devise precise and undetectable attacks like targeted phishing attacks, delivering fake news, etc.
- Input attacks/ adversarial attacks: Attackers can feed altered inputs into AI systems, to trigger unexpected/incorrect results.
- Data Poisoning: Malicious actors corrupt AI training data sets by poisoning them with bad data, affecting the system’s accuracy.
Examples of how AI can be weaponized
GPT-2 text generator/ language models
In November 2019, OpenAI released the latest and largest version of GPT-2 (Generative Pretrained Transformer 2). This language model has the training to generate unique textual content, based on a given input. It even tailors the output style and subject based on the input. So, if you input a specific topic or theme, GPT-2 will yield a few lines of text. GPT-2 is exceptional in that it doesn’t produce pre-existing strings, but singular content that didn’t exist before the model created it.
Drawbacks of GPT-2
The language model is built with 1.5 billion parameters and has a “credibility score” of 6.9 out of 10. The model received a training with the help of 8 million text documents. As a result, OpenAI claims that “GPT-2 outperforms other language models.” The text generated by GPT-2 is as good as text composed by a human. Since detecting this synthetic text is challenging, creating spam emails and messages, fake news, or performing targeted phishing attacks, among other things, becomes easier.
Image recognition software
Image recognition is the process of identifying pixels and patterns to detect objects in digital images. The latest smartphones (for biometric authentication), social networking platforms, Google reverse image search, etc. use facial recognition. AI-based face recognition softwares detect faces in the camera’s field of vision. Given its multiple uses across industries and domains, researchers expect the image recognition software market to make a whopping USD 39 billion, by 2021.
Drawbacks of image recognition softwares
Major smartphone brands are now using facial recognition instead of fingerprint recognition, in their biometric authentication systems. Since this cutting-edge technology is popular among consumers, cyber criminals have found ways to exploit it.
- Tricking facial recognition: It has been demonstrated that Apple’s Face ID can be duped using 3D masks. There are also other instances of deceiving facial recognition with infrared lights, glasses, etc. Identical twins, such as myself, can swap our smartphones to trick even the most efficient algorithms, currently available.
- Blocking automated facial recognition: As facial recognition depends on key features of the face, an alteration made to the features can block automated facial recognition. Similarly, researchers are exploring various ways by which automated facial recognition can be blocked.
For example: Researchers found that minor modifications to a stop sign confuses autonomous cars. If implemented in real life, these technologies could have severe consequences.
Poisoned training sets
Machine learning algorithms that power Artificial Intelligence, learn from data sets (training sets) or by extracting patterns from data sets.
Drawbacks of Machine Learning algorithms
Attackers can poison training sets with bad data, to alter a system’s accuracy. They can even “teach” the model to behave differently, through a backdoor or otherwise. As a result, the model fails to work in the intended way, and will remain corrupted.
In the most unusual of ways, Microsoft’ AI chatbot, Tay, was corrupted through Twitter trolls. Releasing the smart chatbot was on an experimental basis, to engage people in “playful conversations.” However, Twitter users deluged the chatbot with racist, misogynistic, and anti-semitic tweets, turning Tay into a mouthpiece for a terrifying ideology in under a day.
What next?
AI is here to stay. So, as we build Artificial Intelligence systems that can efficiently detect and respond to cyber threats, we should take small steps to ensure they are not exploited:
- Focus on basic cybersecurity hygiene including network security and anti-malware systems.
- Ensure there is some human monitoring/ intervention even for the most advanced AI systems.
- Teach AI systems to detect foreign data based on timestamps, data quality etc.