{"id":144483,"date":"2023-12-28T11:33:42","date_gmt":"2023-12-28T11:33:42","guid":{"rendered":"https:\/\/www.techopedia.com\/?post_type=definition&p=144483"},"modified":"2023-12-28T11:33:42","modified_gmt":"2023-12-28T11:33:42","slug":"ai-jacking","status":"publish","type":"definition","link":"https:\/\/www.techopedia.com\/definition\/ai-jacking","title":{"rendered":"AI Jacking"},"content":{"rendered":"

What is AI Jacking?<\/span><\/h2>\n

AI jacking is a new cybersecurity<\/a> term that explains a specific kind of cyberattack<\/a> targeting artificial intelligence<\/a> (AI) systems. It primarily affects popular AI platforms like Hugging Face<\/a>. This kind of attack is concerning because it can affect a lot of users at once.<\/p>\n

Hugging Face is a key site in this issue. It’s known for its open-source<\/a> machine learning<\/a> projects, offering many models and datasets<\/a> used in AI research and development.<\/p>\n

The platform gained more users with the growth of generative AI<\/a>, especially with models like GPT, the basis for OpenAI’s ChatGPT<\/a>. But its popularity also made it a target for AI jacking.<\/p>\n

Techopedia Explains<\/h3>\n

The attack happens when someone maliciously<\/a> takes advantage of the way Hugging Face renames its models or datasets. Normally, when a model or dataset gets a new name, the old name redirects to the new one.<\/p>\n

But if a hacker takes the old name for their use, they can replace the original content with something harmful or incorrect. This is dangerous, especially in machine learning, where data integrity<\/a> is very important.<\/p>\n

How Does AI Jacking Work?<\/span><\/h2>\n

AI jacking operates through a series of targeted steps that exploit the structure and functionalities of AI platforms.<\/p>\n

Identification of Targets<\/strong><\/p>\n

Attackers begin by identifying popular or widely used AI models and datasets within platforms like Hugging Face. They focus on those with significant dependencies in various projects.<\/p>\n

Monitoring for Renaming Events<\/strong><\/p>\n

The attackers closely monitor these AI resources for any renaming events. Such events typically involve changing the name of a model or dataset for reasons like updates, rebranding, or organizational changes.<\/p>\n

Registration of Abandoned Names<\/strong><\/p>\n

Once a renaming event occurs, the original name of the resource becomes potentially available. Attackers swiftly register these abandoned names under their control before they are noticed or blocked by the platform’s administrators.<\/p>\n

Replacement with Malicious Content<\/strong><\/p>\n

After securing control over the old names, attackers replace the legitimate content with malicious versions. These could be subtly altered models or datasets designed to perform malicious functions, gather data illicitly, or corrupt AI training processes<\/a>.<\/p>\n

Exploitation of Dependency Chains<\/strong><\/p>\n

Many AI applications and systems depend on these resources for their functionality. By compromising a single model or dataset, attackers can potentially infiltrate multiple downstream applications and projects that rely on the integrity of these resources.<\/p>\n

Delayed Detection<\/strong><\/p>\n

The changes are often subtle and hard to notice. Because of this, users and developers might not quickly spot the difference, especially if the harmful changes are made to look like normal updates.<\/p>\n

Potential for Widespread Impact<\/strong><\/p>\n

The interconnected nature of AI systems means that a single compromised resource can have a ripple effect, impacting a wide range of applications and users. This potential for widespread impact is what makes AI jacking a particularly insidious form of cyberattack.<\/p>\n

AI Jacking’s Implications and Limitations<\/span><\/h2>\n

AI jacking is a cybersecurity threat with serious consequences for AI, posing various risks and facing some challenges in its execution.<\/p>\n

Impact and Risks<\/h3>\n
\n\n\n\n\n\n\n\n\n
Impact\/Risk<\/th>\nDescription<\/th>\n<\/tr>\n<\/thead>\n
Reduced Trust in AI Platforms<\/strong><\/td>\nAI jacking can make people less confident in using or contributing to AI models and platforms due to security concerns.<\/td>\n<\/tr>\n
Data Integrity Issues<\/strong><\/td>\nThe accuracy of AI depends on good data. AI jacking risks harming this data, leading to flawed AI training and inaccurate results, which is a huge problem in critical areas like healthcare.<\/td>\n<\/tr>\n
Operational Problems for Businesses<\/strong><\/td>\nCompanies using AI can face disruptions from AI jacking, leading to financial loss, work stoppages, and damage to their reputation.<\/td>\n<\/tr>\n
Potential for Spreading False Information<\/strong><\/td>\nAI jacking could be used to spread misinformation through AI systems, impacting public opinion or causing confusion.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Limitations of AI Jacking<\/h3>\n
\n\n\n\n\n\n\n\n
Limitations<\/th>\nDetection and Response Mechanisms<\/th>\n<\/tr>\n<\/thead>\n
Detection and Response Mechanisms<\/strong><\/td>\nAs awareness of AI jacking increases, so do efforts to detect and respond to such attacks. Improved security protocols and AI auditing practices can limit the effectiveness of AI jacking.<\/td>\n<\/tr>\n
Platform Countermeasures<\/strong><\/td>\nAI platforms, alerted to the threat of AI jacking, are likely to implement stronger security measures, making it more challenging for attackers to exploit vulnerabilities successfully.<\/td>\n<\/tr>\n
Legal and Ethical Constraints<\/strong><\/td>\nThe legality and ethical considerations surrounding AI jacking can deter potential attackers. Legal consequences and the growing emphasis on ethical AI<\/a> use to serve as deterrents.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

How Legit Security Uncovered AI Jacking<\/span><\/h2>\n

Legit Security’s discovery of AI jacking<\/a> involved a careful examination of how the Hugging Face platform manages its AI models and datasets.<\/p>\n

Initial Tests<\/h3>\n

The team started by changing their account name on Hugging Face from \u201chigh-rep-account\u201d to \u201cnew-high-rep-account.\u201d They watched how the platform redirected these changes, noting that the original account name became available again. This suggested a possible security issue.<\/p>\n

Demonstrating the Vulnerability<\/h3>\n

To show how AI jacking works, Legit Security made a demonstration video. In it, they took over an existing model and added harmful code, proving the risks of this vulnerability.<\/p>\n

Searching for Vulnerable Projects<\/h3>\n

Hugging Face doesn\u2019t keep a history of changes to its projects like some other platforms do. So, Legit Security used the Wayback Machine<\/a>, a tool that archives the internet<\/a>, to look at past versions of Hugging Face’s models and datasets.<\/p>\n

They focused on changes since 2020 when Hugging Face first started hosting these models and datasets.<\/p>\n

Research Process<\/h3>\n

The team looked at various dates in the Wayback Machine archives and gathered information about the models and organizations of Hugging Face at those times.<\/p>\n

They adjusted their methods to match changes in the appearance of Hugging Face\u2019s website over the years.<\/p>\n

Identifying Risks<\/h3>\n

After collecting names, they checked each one to see if it redirected to a new name. A redirect meant the original name had changed, creating a chance for AI jacking.<\/p>\n

They found many accounts that could be hijacked this way. There might be even more vulnerable accounts, as not all historical data was available in the archives.<\/p>\n

The Bottom Line<\/span><\/h2>\n

In summary, AI jacking is a complex type of cyberattack mainly affecting AI platforms such as Hugging Face. It involves taking over previously used names for AI models and datasets and inserting harmful content into them.<\/p>\n

This attack can damage the trust in AI technologies, affect the quality of AI data, and disrupt business operations.<\/p>\n

Legit Security\u2019s work in uncovering this issue emphasizes the importance of stronger security and continuous monitoring in the AI field.<\/p>\n

As AI becomes more widespread, protecting against threats like AI jacking is key to maintaining safe and responsible AI use.<\/p>\n

\n
\n

References<\/span><\/h2>\n<\/div>\n
\n
    \n
  1. Legit Discovers “AI Jacking” Vulnerability in Popular Hugging Face AI Platform<\/a> (Legit Security)<\/li>\n
  2. Internet Archive: Wayback Machine<\/a> (Wayback Machine)<\/li>\n<\/ol>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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