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DeepSeek
AI Models AI technology AI Threat China Cyber Security Data Mining Data Privacy data security DeepSeek

DeepSeek AI Raises Data Security Concerns Amid Ties to China

 

The launch of DeepSeek AI has created waves in the tech world, offering powerful artificial intelligence models at a fraction of the cost compared to established players like OpenAI and Google. 

However, its rapid rise in popularity has also sparked serious concerns about data security, with critics drawing comparisons to TikTok and its ties to China. Government officials and cybersecurity experts warn that the open-source AI assistant could pose a significant risk to American users. 

On Thursday, two U.S. lawmakers announced plans to introduce legislation banning DeepSeek from all government devices, citing fears that the Chinese Communist Party (CCP) could access sensitive data collected by the app. This move follows similar actions in Australia and several U.S. states, with New York recently enacting a statewide ban on government systems. 

The growing concern stems from China’s data laws, which require companies to share user information with the government upon request. Like TikTok, DeepSeek’s data could be mined for intelligence purposes or even used to push disinformation campaigns. Although the AI app is the current focus of security conversations, experts say that the risks extend beyond any single model, and users should exercise caution with all AI systems. 

Unlike social media platforms that users can consciously avoid, AI models like DeepSeek are more difficult to track. Dimitri Sirota, CEO of BigID, a cybersecurity company specializing in AI security compliance, points out that many companies already use multiple AI models, often switching between them without users’ knowledge. This fluidity makes it challenging to control where sensitive data might end up. 

Kelcey Morgan, senior manager of product management at Rapid7, emphasizes that businesses and individuals should take a broad approach to AI security. Instead of focusing solely on DeepSeek, companies should develop comprehensive practices to protect their data, regardless of the latest AI trend. The potential for China to use DeepSeek’s data for intelligence is not far-fetched, according to cybersecurity experts. 

With significant computing power and data processing capabilities, the CCP could combine information from multiple sources to create detailed profiles of American users. Though this might not seem urgent now, experts warn that today’s young, casual users could grow into influential figures worth targeting in the future. 

To stay safe, experts advise treating AI interactions with the same caution as any online activity. Users should avoid sharing sensitive information, be skeptical of unusual questions, and thoroughly review an app’s terms and conditions. Ultimately, staying informed and vigilant about where and how data is shared will be critical as AI technologies continue to evolve and become more integrated into everyday life.
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TOUXtract Exploits
AI Models Cyber Researchers Cyberattacks CyberCrime Cyberhackers Cyberthreats Technology TOUXtract Exploits

AI Models at Risk from TPUXtract Exploit

 


A team of researchers has demonstrated that it is possible to steal an artificial intelligence (AI) model without actually gaining access to the device that is running the model. The uniqueness of the technique lies in the fact that it works efficiently even if the thief may not have any prior knowledge as to how the AI works in the first place, or how the computer is structured. 

According to North Carolina State University's Department of Electrical and Computer Engineering, the method is known as TPUXtract, and it is a product of their department. With the help of a team of four scientists, who used high-end equipment and a technique known as "online template-building", they were able to deduce the hyperparameters of a convolutional neural network (CNN) running on Google Edge Tensor Processing Unit (TPU), which is the settings that define its structure and behaviour, with a 99.91% accuracy rate. 

The TPUXtract is an advanced side-channel attack technique devised by researchers at the North Carolina State University, designed to protect servers from attacks. A convolutional neural network (CNN) running on a Google Edge Tensor Processing Unit (TPU) is targeted in the attack, and electromagnetic signals are exploited to extract hyperparameters and configurations of the model without the need for previous knowledge of its architecture and software. 

A significant risk to the security of AI models and the integrity of intellectual property is posed by these types of attacks, which manifest themselves across three distinct phases, each of which is based on advanced methods to compromise the AI models' integrity. Attackers in the Profiling Phase observe and capture side-channel emissions produced by the target TPU as it processes known input data as part of the Profiling Phase. As a result, they have been able to decode unique patterns which correspond to specific operations such as convolutional layers and activation functions by using advanced methods like Differential Power Analysis (DPA) and Cache Timing Analysis. 

The Reconstruction Phase begins with the extraction and analysis of these patterns, and they are meticulously matched to known processing behaviours This enables adversaries to make an inference about the architecture of the AI model, including the layers that have been configured, the connections made, and the parameters that are relevant such as weight and bias. Through a series of repeated simulations and output comparisons, they can refine their understanding of the model in a way that enables precise reconstruction of the original model. 

Finally, the Validation Phase ensures that the replicated model is accurate. During the testing process, it is subject to rigorous testing with fresh inputs to ensure that it performs similarly to that of the original, thus providing reliable proof of its success. The threat that TPUXtract poses to intellectual property (IP) is composed of the fact that it enables attackers to steal and duplicate artificial intelligence models, bypassing the significant resources that are needed to develop them.

The competition could recreate and mimic models such as ChatGPT without having to invest in costly infrastructure or train their employees. In addition to IP theft, TPUXtract exposed cybersecurity risks by revealing an AI model's structure that provided visibility into its development and capabilities. This information could be used to identify vulnerabilities and enable cyberattacks, as well as expose sensitive data from a variety of industries, including healthcare and automotive.

Further, the attack requires specific equipment, such as a Riscure Electromagnetic Probe Station, high-sensitivity probes, and Picoscope oscilloscope, so only well-funded groups, for example, corporate competitors or state-sponsored actors, can execute it. As a result of the technical and financial requirements for the attack, it can only be executed by well-funded groups. With the understanding that any electronic device will emit electromagnetic radiation as a byproduct of its operations, the nature and composition of that radiation will be affected by what the device does. 

To conduct their experiments, the researchers placed an EM probe on top of the TPU after removing any obstructions such as cooling fans and centring it over the part of the chip emitting the strongest electromagnetic signals. The machine then emitted signals as a result of input data, and the signals were recorded. The researchers used the Google Edge TPU for this demonstration because it is a commercially available chip that is widely used to run AI models on edge devices meaning devices utilized by end users in the field, as opposed to AI systems that are used for database applications. During the demonstration, electromagnetic signals were monitored as a part of the technique used to conduct the demonstration.

A TPU chip was placed on top of a probe that was used by researchers to determine the structure and layer details of an AI model by recording changes in the electromagnetic field of the TPU during AI processing. The probe provided real-time data about changes in the electromagnetic field of the TPU during AI processing. To verify the model's electromagnetic signature, the researchers compared it to other signatures made by AI models made on a similar device - in this case, another Google Edge TPU. Using this technique, Kurian says, AI models can be stolen from a variety of different devices, including smartphones, tablets and computers. 

The attacker should be able to use this technique as long as they know the device from which they want to steal, have access to it while it is running an AI model, and have access to another device with similar specifications According to Kurian, the electromagnetic data from the sensor is essentially a ‘signature’ of the way AI processes information. There is a lot of work that goes into pulling off TPUXtract. The process not only requires a great deal of technical expertise, but it also requires a great deal of expensive and niche equipment as well. To scan the chip's surface, NCSU researchers used a Riscure EM probe station equipped with a motorized XYZ table, and a high-sensitivity electromagnetic probe to capture the weak signals emanating from it. 

It is said that the traces were recorded using a Picoscope 6000E oscilloscope, and Riscure's icWaves FPGA device aligned them in real-time, and the icWaves transceiver translated and filtered out the irrelevant signals using bandpass filters and AM/FM demodulation, respectively. While this may seem difficult and costly for a hacker to do on their own, Kurian explains, "It is possible for a rival company to do this within a couple of days, regardless of how difficult and expensive it will be. 

Taking the threat of TPUXtract into account, this model poses a formidable challenge to AI model security, highlighting the importance of proactive measures. As an organization, it is crucial to understand how such attacks work, implement robust defences, and ensure that they can safeguard their intellectual property while maintaining trust in their artificial intelligence systems. The AI and cybersecurity communities must learn continuously and collaborate to stay ahead of the changing threats as they arise.
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Technology
AI Ecosystem AI Models AI technology AI Tool Computing Data Privacy Data Sets DeFi privacy protection secure computing Technology

The Role of Confidential Computing in AI and Web3

 

 
The rise of artificial intelligence (AI) has amplified the demand for privacy-focused computing technologies, ushering in a transformative era for confidential computing. At the forefront of this movement is the integration of these technologies within the AI and Web3 ecosystems, where maintaining privacy while enabling innovation has become a pressing challenge. A major event in this sphere, the DeCC x Shielding Summit in Bangkok, brought together more than 60 experts to discuss the future of confidential computing.

Pioneering Confidential Computing in Web3

Lisa Loud, Executive Director of the Secret Network Foundation, emphasized in her keynote that Secret Network has been pioneering confidential computing in Web3 since its launch in 2020. According to Loud, the focus now is to mainstream this technology alongside blockchain and decentralized AI, addressing concerns with centralized AI systems and ensuring data privacy.

Yannik Schrade, CEO of Arcium, highlighted the growing necessity for decentralized confidential computing, calling it the “missing link” for distributed systems. He stressed that as AI models play an increasingly central role in decision-making, conducting computations in encrypted environments is no longer optional but essential.

Schrade also noted the potential of confidential computing in improving applications like decentralized finance (DeFi) by integrating robust privacy measures while maintaining accessibility for end users. However, achieving a balance between privacy and scalability remains a significant hurdle. Schrade pointed out that privacy safeguards often compromise user experience, which can hinder broader adoption. He emphasized that for confidential computing to succeed, it must be seamlessly integrated so users remain unaware they are engaging with such technologies.

Shahaf Bar-Geffen, CEO of COTI, underscored the role of federated learning in training AI models on decentralized datasets without exposing raw data. This approach is particularly valuable in sensitive sectors like healthcare and finance, where confidentiality and compliance are critical.

Innovations in Privacy and Scalability

Henry de Valence, founder of Penumbra Labs, discussed the importance of aligning cryptographic systems with user expectations. Drawing parallels with secure messaging apps like Signal, he emphasized that cryptography should function invisibly, enabling users to interact with systems without technical expertise. De Valence stressed that privacy-first infrastructure is vital as AI’s capabilities to analyze and exploit data grow more advanced.

Other leaders in the field, such as Martin Leclerc of iEXEC, highlighted the complexity of achieving privacy, usability, and regulatory compliance. Innovative approaches like zero-knowledge proof technology, as demonstrated by Lasha Antadze of Rarimo, offer promising solutions. Antadze explained how this technology enables users to prove eligibility for actions like voting or purchasing age-restricted goods without exposing personal data, making blockchain interactions more accessible.

Dominik Schmidt, co-founder of Polygon Miden, reflected on lessons from legacy systems like Ethereum to address challenges in privacy and scalability. By leveraging zero-knowledge proofs and collaborating with decentralized storage providers, his team aims to enhance both developer and user experiences.

As confidential computing evolves, it is clear that privacy and usability must go hand in hand to address the needs of an increasingly data-driven world. Through innovation and collaboration, these technologies are set to redefine how privacy is maintained in AI and Web3 applications.

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Google Cloud
AI Models AI technology Cyber Security Data integrity data poisoning data security Generative AI Google Cloud

Securing Generative AI: Tackling Unique Risks and Challenges

 

Generative AI has introduced a new wave of technological innovation, but it also brings a set of unique challenges and risks. According to Phil Venables, Chief Information Security Officer of Google Cloud, addressing these risks requires expanding traditional cybersecurity measures. Generative AI models are prone to issues such as hallucinations—where the model produces inaccurate or nonsensical content—and the leaking of sensitive information through model outputs. These risks necessitate the development of tailored security strategies to ensure safe and reliable AI use. 

One of the primary concerns with generative AI is data integrity. Models rely heavily on vast datasets for training, and any compromise in this data can lead to significant security vulnerabilities. Venables emphasizes the importance of maintaining the provenance of training data and implementing controls to protect its integrity. Without proper safeguards, models can be manipulated through data poisoning, which can result in the production of biased or harmful outputs. Another significant risk involves prompt manipulation, where adversaries exploit vulnerabilities in the AI model to produce unintended outcomes. 

This can include injecting malicious prompts or using adversarial tactics to bypass the model’s controls. Venables highlights the necessity of robust input filtering mechanisms to prevent such manipulations. Organizations should deploy comprehensive logging and monitoring systems to detect and respond to suspicious activities in real time. In addition to securing inputs, controlling the outputs of AI models is equally critical. Venables recommends the implementation of “circuit breakers”—mechanisms that monitor and regulate model outputs to prevent harmful or unintended actions. This ensures that even if an input is manipulated, the resulting output is still within acceptable parameters. Infrastructure security also plays a vital role in safeguarding generative AI systems. 

Venables advises enterprises to adopt end-to-end security practices that cover the entire lifecycle of AI deployment, from model training to production. This includes sandboxing AI applications, enforcing the least privilege principle, and maintaining strict access controls on models, data, and infrastructure. Ultimately, securing generative AI requires a holistic approach that combines innovative security measures with traditional cybersecurity practices. 

By focusing on data integrity, robust monitoring, and comprehensive infrastructure controls, organizations can mitigate the unique risks posed by generative AI. This proactive approach ensures that AI systems are not only effective but also safe and trustworthy, enabling enterprises to fully leverage the potential of this groundbreaking technology while minimizing associated risks.
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Zero Trust
AI Models Cyber Security Data Encryption log files threats Zero Trust

Zero-Trust Log Intelligence: Safeguarding Data with Secure Access

 


Over the years, zero trust has become a popular model adopted by organisations due to a growing need to ensure confidential information is kept safe, an aspect that organisations view as paramount in cybersecurity. Zero-trust is a vital security framework that is fundamentally not like the traditional security perimeter-based model. Instead of relying on a robust boundary, zero-trust grants access to its resources after the constant validation of any user and every device they use, regardless of an individual's position within an organisation or the number of years since one first employed with the company. This "never trust, always verify" policy only grants minimum access to someone, even a long-tenured employee, about what is needed to fulfil their tasks. Because information for cybersecurity is often log file data, zero trust principles can provide better safeguarding of this sensitive information.

Log Files: Why They Are Both Precious and Vulnerable

Log files contain information that reflects all the digital interplay happening on the network, hence can indicate any vulnerability on a system for remediation purposes. For example, it's a good source where one will trace how companies' activities go regarding their performance by analysing log files for anything out of place or anomalies in systems' behaviours for speedy intervention for security lapses. At the same time, however, these log files can expose organisations to vulnerabilities when wrong hands gain access because of possible theft of confidential data or the intention of hacking or modification. The log files have to be strictly controlled and limited only for authorization, because the misuse has to be avoided for maintaining the network secure.

Collecting and Storing Log Data Securely

Zero trust can best be implemented only if gathering and storing of log file collection and storage are sound. It ensures that the real-time data is collected in an environment that has a tamper-resistant place that prevents data from unauthorised modification. Of late, there has been OpenTelemetry, which is gaining popularity due to its potential in the multiple data sources and secure integration with many databases, mostly PostgreSQL.

Secure log storage applies blockchain technology. A decentralised, immutable structure like blockchain ensures logs cannot be altered and their records will remain transparent as well as tamper-proof. The reason blockchain technology works through multiple nodes rather than one central point makes it nearly impossible to stage a focused attack on the log data.

Imposing Least Privilege Access Control

Least privilege access would be one of the greatest principles of zero-trust security, which means that end-users would have only access to what is required to achieve their task. However, it can be challenging when balancing this principle with being efficient in log analysis; traditional access control methods-such as data masking or classification-frequently fall short and are not very practical. One promising solution to this problem is homomorphic encryption, which enables analysis of data in its encrypted state. Analysts can evaluate log files without ever directly seeing the unencrypted data, ensuring that security is maintained without impacting workflow.

Homomorphic encryption is beyond the level of the analyst. This means other critical stakeholders, such as administrators, have access to permissions but are not allowed to read actual data. This means logs are going to be secure at internal teams and thus there is a lesser chance of accidental exposure.

In-House AI for Threat Detection

Companies can further secure log data by putting in-house AI models which are run directly within their database and hence minimise external access. For instance, the company can use a private SLM AI that was trained specifically to analyse the logs. This ensures there is safe and accurate threat detection without having to share any logs with third-party services. The other advantage that an AI trained on relevant log data provides is less bias, as all operations depend on only relevant encrypted log data that can give an organisation precise and relevant insights.

Organisations can ensure maximum security while minimising exposure to potential cyber threats by applying a zero-trust approach through strict access controls and keeping data encrypted all through the analysis process.

Zero-Trust for Optimal Log Security

One of the effective log file intelligence approaches appears to be zero trust security-a security approach that uses the technologies of blockchain and homomorphic encryption to ensure the integrity and privacy of information in management. It means one locks up logs, and it is a source for valuable security insights, kept well protected against unauthorised access and modifications.

Even if an organisation does not adopt zero-trust completely for its systems, it should still ensure that the protection of the logs is considered a priority. By taking the essential aspects of zero-trust, such as having minimal permissions and secured storage, it can help organisations decrease their vulnerability to cyber attacks while protecting this critical source of data.




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RCE
AI Models Cross Site Scripting Cyber Security Enterprise security Jailbreak Tool Jailbreaking LLM LLM security RCE

Managing LLM Security Risks in Enterprises: Preventing Insider Threats

 

Large language models (LLMs) are transforming enterprise automation and efficiency but come with significant security risks. These AI models, which lack critical thinking, can be manipulated to disclose sensitive data or even trigger actions within integrated business systems. Jailbreaking LLMs can lead to unauthorized access, phishing, and remote code execution vulnerabilities. Mitigating these risks requires strict security protocols, such as enforcing least privilege, limiting LLM actions, and sanitizing input and output data. LLMs in corporate environments pose threats because they can be tricked into sharing sensitive information or be used to trigger harmful actions within systems. 

Unlike traditional tools, their intelligent, responsive nature can be exploited through jailbreaking—altering the model’s behavior with crafted prompts. For instance, LLMs integrated with a company’s financial system could be compromised, leading to data manipulation, phishing attacks, or broader security vulnerabilities such as remote code execution. The severity of these risks grows when LLMs are deeply integrated into essential business operations, expanding potential attack vectors. In some cases, threats like remote code execution (RCE) can be facilitated by LLMs, allowing hackers to exploit weaknesses in frameworks like LangChain. This not only threatens sensitive data but can also lead to significant business harm, from financial document manipulation to broader lateral movement within a company’s systems.  

Although some content-filtering and guardrails exist, the black-box nature of LLMs makes specific vulnerabilities challenging to detect and fix through traditional patching. Meta’s Llama Guard and other similar tools provide external solutions, but a more comprehensive approach is needed to address the underlying risks posed by LLMs. To mitigate the risks, companies should enforce strict security measures. This includes applying the principle of least privilege—restricting LLM access and functionality to the minimum necessary for specific tasks—and avoiding reliance on LLMs as a security perimeter. 

Organizations should also ensure that input data is sanitized and validate all outputs for potential threats like cross-site scripting (XSS) attacks. Another important measure is limiting the actions that LLMs can perform, preventing them from mimicking end-users or executing actions outside their intended purpose. For cases where LLMs are used to run code, employing a sandbox environment can help isolate the system and protect sensitive data. 

While LLMs bring incredible potential to enterprises, their integration into critical systems must be carefully managed. Organizations need to implement robust security measures, from limiting access privileges to scrutinizing training data and ensuring that sensitive data is protected. This strategic approach will help mitigate the risks associated with LLMs and reduce the chance of exploitation by malicious actors.
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Technology
AI Models Artifcial Intelligence ChatGPT Software Technology

Want to Make the Most of ChatGPT? Here Are Some Go-To Tips

 







Within a year and a half, ChatGPT has grown from an AI prototype to a broad productivity assistant, even sporting its text and code editor called Canvas. Soon, OpenAI will add direct web search capability to ChatGPT, putting the platform at the same table as Google's iconic search. With these fast updates, ChatGPT is now sporting quite a few features that may not be noticed at first glance but are deepening the user experience if one knows where to look.

This is the article that will teach you how to tap into ChatGPT, features from customization settings to unique prompting techniques, and not only five must-know tips will be useful in unlocking the full range of abilities of ChatGPT to any kind of task, small or big.


1. Rename Chats for Better Organisation

A new conversation with ChatGPT begins as a new thread, meaning that it will remember all details concerning that specific exchange but "forget" all the previous ones. This way, you can track the activities of current projects or specific topics because you can name your chats. The chat name that it might try to suggest is related to the flow of the conversation, and these are mostly overlooked contexts that users need to recall again. Renaming your conversations is one simple yet powerful means of staying organised if you rely on ChatGPT for various tasks.

To give a name to a conversation, tap the three dots next to the name in the sidebar. You can also archive older chats to remove them from the list without deleting them entirely, so you don't lose access to the conversations that are active.


2. Customise ChatGPT through Custom Instructions

Custom Instructions in ChatGPT is a chance to make your answers more specific to your needs because you will get to share your information and preferences with the AI. This is a two-stage personalization where you are explaining to ChatGPT what you want to know about yourself and, in addition, how you would like it to be returned. For instance, if you ask ChatGPT for coding advice several times a week, you can let the AI know what programming languages you are known in or would like to be instructed in so it can fine-tune the responses better. Or, you should be able to ask for ChatGPT to provide more verbose descriptions or to skip steps in order to make more intuitive knowledge of a topic.

To set up personal preferences, tap the profile icon on the upper right, and then from the menu, "Customise ChatGPT," and then fill out your preferences. Doing this will enable you to get responses tailored to your interests and requirements.


3. Choose the Right Model for Your Use

If you are a subscriber to ChatGPT Plus, you have access to one of several AI models each tailored to different tasks. The default model for most purposes is GPT-4-turbo (GPT-4o), which tends to strike the best balance between speed and functionality and even supports other additional features, including file uploads, web browsing, and dataset analysis.

However, other models are useful when one needs to describe a rather complex project with substantial planning. You may initiate a project using o1-preview that requires deep research and then shift the discussion to GPT-4-turbo to get quick responses. To switch models, you can click on the model dropdown at the top of your screen or type in a forward slash (/) in the chat box to get access to more available options including web browsing and image creation.


4. Look at what the GPT Store has available in the form of Mini-Apps

Custom GPTs, and the GPT Store enable "mini-applications" that are able to extend the functionality of the platform. The Custom GPTs all have some inbuilt prompts and workflows and sometimes even APIs to extend the AI capability of GPT. For instance, with Canva's GPT, you are able to create logos, social media posts, or presentations straight within the ChatGPT portal by linking up the Canva tool. That means you can co-create visual content with ChatGPT without having to leave the portal.

And if there are some prompts you often need to apply, or some dataset you upload most frequently, you can easily create your Custom GPT. This would be really helpful to handle recipes, keeping track of personal projects, create workflow shortcuts and much more. Go to the GPT Store by the "Explore GPTs" button in the sidebar. Your recent and custom GPTs will appear in the top tab, so find them easily and use them as necessary.


5. Manage Conversations with a Fresh Approach

For the best benefit of using ChatGPT, it is key to understand that every new conversation is an independent document with its "memory." It does recall enough from previous conversations, though generally speaking, its answers depend on what is being discussed in the immediate chat. This made chats on unrelated projects or topics best started anew for clarity.

For long-term projects, it might even be logical to go on with a single thread so that all relevant information is kept together. For unrelated topics, it might make more sense to start fresh each time to avoid confusion. Another way in which archiving or deleting conversations you no longer need can help free up your interface and make access to active threads easier is


What Makes AI Unique Compared to Other Software?

AI performs very differently from other software in that it responds dynamically, at times providing responses or "backtalk" and does not simply do what it is told to do. Such a property leads to some trial and error to obtain the desired output. For instance, one might prompt ChatGPT to review its own output as demonstrated by replacing single quote characters by double quote characters to generate more accurate results. This is similar to how a developer optimises an AI model, guiding ChatGPT to "think" through something in several steps.

ChatGPT Canvas and other features like Custom GPTs make the AI behave more like software in the classical sense—although, of course, with personality and learning. If ChatGPT continues to grow in this manner, features such as these may make most use cases easier and more delightful.

Following these five tips should help you make the most of ChatGPT as a productivity tool and keep pace with the latest developments. From renaming chats to playing around with Custom GPTs, all of them add to a richer and more customizable user experience.


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Technology
AI Models data poisoning JFrog Security Machine learning Technology

Data Poisoning: The Hidden Threat to AI Models



As ongoing developments in the realms of artificial intelligence and machine learning take place at a dynamic rate, yet another new form of attack is emerging, one which can topple all those systems we use today without much ado: data poisoning. This type of attack involves tampering with data used by AI models in training to make them malfunction, often undetectably. The issue came to light when recently, more than 100 malicious models were uncovered on the popular repository for AI, Hugging Face, by a software management company, JFrog. 

What is Data Poisoning?

Data poisoning is an attack method on AI models by corrupting the data used for its training. In other words, the intent is to have the model make inappropriate predictions or choices. Besides, unlike traditional hacking, it doesn't require access to the system; therefore, data poisoning manipulates input data either before the deployment of an AI model or after the deployment of the AI model, and that makes it very difficult to detect.

One attack happens at the training phase when an attacker manages to inject malicious data into any AI model. Yet another attack happens post-deployment when poisoned data is fed to the AI; it yields wrong outputs. Both kinds of attacks remain hardly detectable and cause damage to the AI system in the long run.

According to research by JFrog, investigators found a number of suspicious models uploaded to Hugging Face, a community where users can share AI models. Those contained encoded malicious code, which the researchers believe hackers-those potentially coming from the KREOnet research network in Korea-might have embedded. The most worrying aspect, however, was the fact that these malicious models went undetected by masquerading as benign.

That's a serious threat because many AI systems today use a great amount of data from different sources, including the internet. In cases where attackers manage to change the data used in the training of a model, that could mean anything from misleading results to actual large-scale cyberattacks.

Why It's Hard to Detect

One of the major challenges with data poisoning is that AI models are built by using enormous data sets, which makes it difficult for researchers to always know what has gone into the model. A lack of clarity of this kind in turn creates ways in which attackers can sneak in poisoned data without being caught.

But it gets worse: AI systems that scrape data from the web continuously in order to update themselves could poison their own training data. This sets up the alarming possibility of an AI system's gradual breakdown, or "degenerative model collapse."

The Consequences of Ignoring the Threat

If left unmitigated, data poisoning could further allow attackers to inject stealth backdoors in AI software that enable them to conduct malicious actions or cause any AI system to behave in ways unexpected. Precisely, they can run malicious code, allow phishing, and rig AI predictions for various nefarious uses.

The cybersecurity industry must take this as a serious threat since more dependence occurs on generative AI linked together, alongside LLMs. If one fails to do so, widespread vulnerability across the complete digital ecosystem will result.

How to Defend Against Data Poisoning

The protection of AI models against data poisoning calls for vigilance throughout the process of the AI development cycle. Experts say that this may require oversight by organisations in using only data from sources they can trust for training the AI model. The Open Web Application Security Project, or OWASP, has provided a list of some best ways to avoid data poisoning; a few of these include frequent checks to find biases and abnormalities during the training of data.

Other recommendations come in the form of multiple AI algorithms that verify results against each other to locate inconsistency. If an AI model starts producing strange results, fallback mechanisms should be in place to prevent any harm.

This also encompasses simulated data poisoning attacks run by cybersecurity teams to test their AI systems for robustness. While it is hard to build an AI system that is 100% secure, frequent validation of predictive outputs goes a long way in detecting and preventing poisoning.

Creating a Secure Future for AI

While AI keeps evolving, there is a need to instil trust in such systems. This will only be possible when the entire ecosystem of AI, even the supply chains, forms part of the cybersecurity framework. This would be achievable through monitoring inputs and outputs against unusual or irregular AI systems. Therefore, organisations will build robust, and more trustworthy models of AI.

Ultimately, the future of AI hangs in the balance with our capability to race against emerging threats like data poisoning. In sum, the ability of businesses to proactively take steps toward the security of AI systems today protects them from one of the most serious challenges facing the digital world.

The bottom line is that AI security is not just about algorithms; it's about the integrity for the data powering those algorithms.


 

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