“Poisoning” - Lessons from A Wiseman's Story about AI “Poisoning” Attacks

The Bedtime Story:

Once, there lived a wise man in a small village. On one occasion, he was rewarded with a goat for his services by a wealthy man. The wise man was happy to get a goat as the reward, which, he can sell to make money.

On the way, three crooks saw the wise man taking the goat. They wanted to cheat the wise man so that they could take away the goat. They devised a plan to get the goat by fooling the wise man, they got separated from one another and took hiding positions at different places on the way of the wise man.

As soon as, the wise man arrived at a lonely place, one of the crooks came out of his hiding place and asked the wise man in a shocking manner, “Sir, what are you doing? I don't understand why a pious man like you need to carry a dog on his shoulders?" The wise man was surprised to hear such words. He screamed, “Can't you see? It's not a dog but a goat, you stupid fool". The cheat replied,” Sir, I beg your pardon. I told you what I saw. I am sorry if you don’t believe it.” The wise man was annoyed at the discrepancy but started his journey once again.

The wise man had barely walked a distance, when another cheat came out of his hiding place and asked the wise man, "Sir, what are you doing? I don't understand why you needs to carry a dog on your shoulders. You seem to be a wise person. Such an act is pure stupidity on your part." The wise man yelled, "What? How can you mistake a goat for a dog?" The second cheat replied, ""Sir, you seem to be highly mistaken in this regard. Either you don’t know how goat looks like or you are doing it knowingly. I just told you what I saw. Thank you". The second cheat went away smiling. The wise man got confused but continued to walk further.

Again, the wise man had covered a little distance when the third cheat met him. The third cheat asked laughingly, "Sir, why do you carry a dog on your shoulders? It makes you a laughingstock”. Hearing the words of the third thug, the wise man became worried.

He started thinking, “Is it really not a goat but a dog?". He started feeling that the animal he was carrying might really be a dog. The wise man got carried away to such an extent that he hurled the goat on the roadside and ran away. The three crooks laughed at the wise man, they caught the goat and were happy to feast on it.

About the story:

This is a famous bedtime store from Panchatantra. Parents and grandparents are telling their kids for ages in India with a moral “One should not be blindly carried away by what others say.” “If you believe in what others say, without verifying and basing your judgement on the trusted facts, people can easily mislead you”.

Now, let’s get into the topic “Poisoning”:

The bedtime story is talking about an attack in different context, but the core point it is influencing is by attacking on the facts “Learned” by the person by “Poisoning” them. More and more, the people were able to emphasize it as a “Dog” instead of a “Goat”, the wise man started inclining to believe it as a dog and finally treated it as a dog, which he doesn’t want. This happens in the technology world as well.

In the technology world is the Artificial Intelligence systems with the 3 crooks being the malicious attackers and the steps they were using as various types of poisoning attacks which can be planned on AI systems.

AI and Poisoning

In the grand fabric of technological evolution, Artificial Intelligence (AI) stands as one of humanity's most remarkable achievements. It's the digital genius behind self-driving cars that navigate city streets flawlessly, the smart curator of our online experiences, and the engine that propels us into the boundless realm of the future. AI has indeed woven itself into the fabric of our lives, raising humanity to unprecedented heights of convenience, efficiency, and innovation.

But there's a darker side to this digital juggernaut, one that shrouds the stunning potential of AI in shadows of uncertainty and fear. Image this: the exact technology that fluently predicts your preferences, manages your funds, and even diagnoses medical conditions turns against you. The AI systems we've come to rely on, those digital marvels that seemed unfailing, can be stealthily poisoned. And when they are, the consequences are nothing short of a digital nightmare.

In this writeup through the annals of AI, I will divulge a chilling truth: AI poisoning attacks, the stealthy assaults on our trusted digital partners. These attacks shatter the illusion of AI invincibility, revealing a dangerous world where deception rules supreme. The consequences of such ploy ripple far beyond mere inconvenience; they threaten the very essence of our privacy, security, and safety.

But I won't leave you in the dark. I'll navigate this treacherous terrain together, exploring not only the terrors of AI poisoning but also the winning strategies to defend against it. We'll uncover possible attack vectors, ways to prevent them, how to detect and protect, and the crucial art of responding when the digital walls crumble. And, if all else fails, we'll ponder how to adapt and thrive in a world where the digital giant has been rendered dormant.

AI poisoning attacks, also known as data poisoning attacks, are a class of cybersecurity threats aimed at manipulating the training data of artificial intelligence (AI) systems to compromise their performance or behavior. These attacks can have serious consequences, as they can lead to incorrect predictions, biased decisions, and even security breaches. Here are various kinds of AI poisoning attacks:

Type of Poisoning Attack

Label Flipping (Data Poisoning): In this attack, an adversary deliberately mislabels data points in the training dataset. By injecting incorrect labels, the attacker can manipulate the AI model to produce incorrect predictions during inference. For example, changing the label of a stop sign image to that of a yield sign could lead to dangerous consequences in autonomous vehicles.

Data Inference Attacks: Instead of modifying the training data, an attacker may try to infer sensitive information about the training data used to create an AI model. By making a series of queries to the model and analysing its responses, the attacker aims to reverse-engineer information about the training data, which could be used for various malicious purposes.

Data Poisoning with Adversarial Samples: Adversarial samples are specially crafted inputs that are designed to mislead an AI model. In data poisoning attacks, adversaries may inject these adversarial samples into the training data to corrupt the model's learning process. The model can become more vulnerable to adversarial attacks during inference as a result.

Membership Inference Attacks: In this attack, the adversary tries to determine whether a specific data point was part of the training dataset used to build the AI model. This can be done by querying the model with data points and analysing its responses. Successful membership inference attacks can have privacy implications, especially in contexts involving sensitive data.

Data Distribution Attacks: In some cases, attackers may attempt to manipulate the distribution of training data. They can add or remove certain types of data to bias the model's predictions in a way that benefits the attacker's interests. For example, a spam email classifier could be manipulated to let through malicious emails.

Model Inversion Attacks: These attacks aim to reverse-engineer an AI model's internal parameters or the training data it was exposed to. Attackers can use the model's predictions to infer sensitive information about its training data, potentially revealing confidential information or trade secrets.

Backdoor Attacks: Backdoor attacks involve adding hidden patterns or triggers to the training data in such a way that the AI model exhibits a specific behaviour when presented with these patterns during inference. This behaviour can be malicious and harmful, such as granting unauthorized access to a system.

Stochastic Gradient Descent (SGD) Attacks: Attackers can manipulate the optimization process during model training by injecting carefully crafted gradients into the training process. This can lead to subtle but harmful changes in the model's behaviour during inference.

Feature Attribute Attacks: In this type of attack, adversaries manipulate or perturb specific features or attributes of the training data to force the AI model to make biased or incorrect predictions. For example, changing the gender attribute in a hiring model to favor a particular gender.

Sybil Attacks: Sybil attacks involve creating multiple fake identities or data sources to influence the training process. By flooding the training dataset with fake data, attackers can distort the model's understanding of the underlying data distribution.

Evasion Attacks: While not strictly poisoning, evasion attacks involve crafting inputs that mislead an AI model during inference. Attackers generate inputs designed to cause the model to make incorrect predictions, potentially bypassing security measures.

As we know different kinds of poisoning attacks which can be possible on AI systems, let's discuss now about the key aspects – how can we prevent, detect and protect and also see, how can we respond to the AI poisoning attacks.

Preventing

Preventing AI poisoning attacks is a challenging task, but there are several strategies and best practices that can help mitigate the risk. These attacks aim to manipulate the training data of machine learning models to compromise their performance or introduce malicious behaviour. Here are some steps to prevent AI poisoning attacks:

Data Quality and Validation:

Data Scrutiny: Carefully vet and clean your training data to remove any potentially harmful or malicious data points.

Data Anomaly Detection: Implement anomaly detection techniques to identify and filter out data points that exhibit suspicious patterns or behaviour.

Data Diversity: Ensure your training data is diverse and representative of the real-world scenarios the AI system will encounter. A more diverse dataset is less susceptible to manipulation.

Secure Data Storage:

Data Encryption: Store your training data securely using encryption and access controls to prevent unauthorized tampering.

Audit Logs: Maintain detailed logs of data access and modifications to track any suspicious activity.

Access Control:

Limit Access: Restrict access to your training data and model parameters to authorized personnel only.

Authentication: Implement strong authentication mechanisms to ensure that only trusted individuals can access and modify data.

Model Robustness:

Adversarial Training: Train your AI models with adversarial examples to make them more robust against poisoning attacks.

Out-of-Distribution Detection: Implement techniques for detecting when the model is presented with data that falls outside the training distribution, which can be an indicator of a poisoning attack.

Continuous Monitoring:

Real-time Monitoring: Monitor the performance of your AI models in real-time. Sudden drops in performance or unusual behaviour can indicate an attack.

Model Updating: Regularly update your models with fresh, clean data to reduce the impact of any successful poisoning attacks.

User Education:

Security Awareness: Train personnel to recognize and report suspicious data or model behaviour.

Phishing Awareness: Educate users about phishing attacks that might be used to gain unauthorized access to your data.

Third-Party Data Validation:

Third-party Data Sources: If you use external data sources, ensure they have robust security measures in place and validate the data to reduce the risk of poisoning.

Regular Security Audits:

Penetration Testing: Conduct penetration tests and security audits to identify vulnerabilities in your AI system's infrastructure.

Regulatory Compliance:

Compliance Frameworks: Ensure that your AI systems comply with relevant data protection and cybersecurity regulations.

Collaboration and Research:

Collaboration with Researchers: Engage with the AI research community to stay informed about the latest advances in AI security and adversarial attack prevention.

Post-Deployment Response Plan:

Incident Response: Develop an incident response plan that outlines the steps to take if an AI poisoning attack is suspected or detected.

We need to remember that AI poisoning attacks are evolving, and new attack techniques may emerge over time. Staying vigilant and proactive in your approach to AI security is crucial to reduce the risk of such attacks and their potential impact on your AI systems.

Detecting and Protecting

There are some common principles that apply to both detecting and protecting AI models from poisoning attacks. Let's explore those common aspects first before going into those applicable specifically to Detecting or Protecting:

Data Quality and Validation:

Detection: Analyse training data for anomalies, unusual patterns, or outliers that might indicate poisoned data.

Protection: Clean and pre-process training data to remove malicious data points. Implement data validation techniques to ensure the authenticity and quality of the data.

Robust Model Training:

Detection: Test models with adversarial examples to identify vulnerabilities to poisoning attacks.

Protection: Train models with adversarial examples to enhance their resilience against adversarial inputs.

Monitoring and Alerting:

Detection: Monitor model behaviour and performance for sudden changes or anomalies.

Protection: Implement real-time monitoring and alert systems to detect unusual model behaviour that might indicate a poisoning attack.

User Access Control:

Detection: Monitor user access to the model and data to identify unauthorized or suspicious activities.

Protection: Restrict access to authorized personnel only. Implement strong authentication and access controls.

Model Behaviour Analysis:

Detection: Analyse model predictions and outputs for unexpected behaviours or patterns.

Protection: Regularly review model outputs for anomalies and discrepancies that might indicate a poisoning attack.

Data Diversity:

Detection: Compare model behaviour on different subsets of data to identify inconsistencies that may be due to poisoning.

Protection: Collect diverse and representative training data to make the model less susceptible to targeted attacks.

Adversarial Training:

Detection: Test models with adversarial examples to assess their robustness against poisoning attacks.

Protection: Train models with adversarial examples to improve their resistance to adversarial inputs.

Regular Model Updates:

Detection: Regularly update models with fresh, clean data to mitigate the impact of any successful poisoning attacks.

Protection: Periodically retrain models with the latest data to maintain model accuracy and security.

Collaborative Approach:

Detection: Collaborate with researchers and the AI community to stay informed about emerging attack techniques.

Protection: Engage in collaboration to share knowledge and strategies for protecting against poisoning attacks.

Incident Response Plan:

Detection: Have a well-defined incident response plan to address and mitigate the impact of detected poisoning attacks.

Protection: Develop a response plan to minimize damage in case of a successful poisoning attack and ensure quick recovery.

By intertwining these strategies, you can create a comprehensive approach that not only helps in detecting and responding to poisoning attacks but also prevents them from occurring or being successful in the first place. Remember that a multi-layered defence strategy is crucial for effective protection against AI poisoning attacks.

Apart from those common, there are some specific techniques and strategies that can be more focused on either detecting or protecting against AI poisoning attacks. Let's delve into the deltas, or specific approaches, for each:

Specific Techniques for Detecting AI Poisoning Attacks:

Outlier Detection: Detect unusual data points during training that might indicate poisoning. This is primarily a detection technique.

Pattern Recognition: Analyse patterns in model predictions and outputs, looking for deviations from expected behaviour, which is geared toward detection.

Adversarial Testing: Test models with known adversarial examples to identify vulnerabilities. This is primarily a detection-focused approach.

Real-time Monitoring and Alerts: Set up real-time monitoring and alerting systems to quickly detect and respond to anomalies in model behaviour or performance, emphasizing detection.

Specific Techniques for Protecting AI Models from Poisoning Attacks:

Data Augmentation: Introduce synthetic data or data from trusted sources to dilute the impact of poisoned data. This is a protective measure aimed at making the model more resilient.

Regularization Techniques: Apply techniques like dropout, weight decay, or adversarial training during model training to improve model robustness and protect against poisoning.

Input Validation Checks: Before input data reaches the model, implement checks to ensure it adheres to predefined rules, which is a protective measure.

User Access Control: Restrict access to your model and training data to authorized personnel only, emphasizing the protection of data and models.

Adversarial Training: Train models with adversarial examples to enhance their resistance to adversarial inputs, primarily a protective measure.

Data Diversity: Collect diverse and representative training data to make the model less susceptible to targeted attacks, a protective strategy.

Collaborative Approach: Collaborate with researchers and the AI community to share knowledge and strategies for protecting against poisoning attacks, which is both protective and proactive.

Incident Response Plan: Develop a response plan to minimize damage in case of a successful poisoning attack and ensure quick recovery, which is a protective measure.

While these techniques may be more focused on detection or protection, it's important to note that a comprehensive approach should incorporate elements of both. Prevention is better than cure, so protective measures should be a priority, but detection mechanisms are crucial for identifying and responding to any potential breaches in your defences. A balanced strategy combining these elements offers the best defence against AI poisoning attacks.

Response

If you suspect that your AI system has been poisoned or compromised in some way, it's essential to take immediate action to mitigate the potential risks. Here is a general response process to follow:

Isolate the AI System: Disconnect the AI system from any networks or external connections to prevent further damage or unauthorized access.

Document Suspicious Activity: Log any unusual behaviour or signs of compromise. This information will be crucial for analysis and investigation.

Alert Relevant Parties: Notify the appropriate individuals or teams within your organization responsible for cybersecurity or IT. This might include your IT department, security officers, or incident response team.

Shut Down the AI System: If possible, shut down the AI system to prevent it from executing any potentially harmful actions. This can help contain the damage.

Preserve Evidence: It's essential to preserve any evidence related to the suspected poisoning. This may include log files, system snapshots, or any data that can help in identifying the source and extent of the compromise.

Investigation: Conduct a thorough investigation into the incident. This may involve analysing logs, reviewing system configurations, and examining any other relevant data. The goal is to determine how the AI system was poisoned, what data or functionality was compromised, and who might be responsible.

Cleanse and Restore: Once the source of poisoning is identified and removed, cleanse the AI system to ensure it's free of any malicious code or vulnerabilities. This may involve reinstalling the software or restoring from a known-good backup.

Implement Security Improvements: Identify any weaknesses or vulnerabilities in your AI system's security that may have been exploited. Implement security enhancements and best practices to prevent future incidents.

Notify Stakeholders: If the AI system was used for critical operations or contained sensitive data, you may need to notify relevant stakeholders, such as customers or partners, about the incident.

Legal and Regulatory Compliance: Depending on the nature of the AI system and the data it handles, you may need to comply with legal and regulatory requirements related to data breaches or cybersecurity incidents.

Monitoring and Prevention: Implement ongoing monitoring and security measures to prevent future poisoning or compromises. Regularly update and patch the AI system to address new vulnerabilities.

Incident Response Plan: Review and update your organization's incident response plan to incorporate lessons learned from the incident. This will help your organization respond more effectively to future incidents.

Remember that responding to a poisoned AI system requires a coordinated effort involving IT, cybersecurity experts, and potentially legal and compliance teams. Rapid response is crucial to minimize the impact and prevent further damage.

Living without AI:

Living without AI, even temporarily, can be challenging in today's technology-driven world. However, it's possible to maintain business operations and daily life with some adjustments. Here are steps to consider if you need to temporarily live without AI due to a poisoning attack or other cybersecurity incident:

Manual Processes: Identify critical AI-dependent processes and develop manual alternatives. This might involve reassigning tasks to human employees or using traditional, non-AI methods to complete essential functions.

Fallback Systems: If possible, have backup systems or processes in place that can be quickly activated when AI systems are unavailable. These systems should be regularly tested to ensure their effectiveness.

Prioritize Tasks: Determine which tasks and functions are most critical to your business or daily life and focus on maintaining those first. Non-essential tasks may need to be temporarily suspended.

Human Expertise: Rely on human expertise and knowledge to bridge the gap. Subject matter experts can provide guidance and assistance in areas where AI was previously used.

Increase Human Resources: If necessary, hire temporary staff or reallocate existing resources to handle increased workloads resulting from the loss of AI functionality.

Data Backup and Retrieval: Ensure that critical data is regularly backed up and can be retrieved without relying on AI systems. This includes customer data, financial records, and any other essential information.

Training and Education: Provide training and support to employees to help them adapt to the temporary loss of AI. Employees may need to learn new procedures or tools to maintain productivity.

Communication: Keep all stakeholders, including employees, customers, and partners, informed about the situation and any changes in operations. Transparency can help manage expectations.

Cybersecurity Measures: Implement additional cybersecurity measures to protect manual processes from potential attacks. With AI systems offline, there may be an increased risk of cyber threats.

Lean on Traditional Tools: Utilize traditional tools and methods that do not rely on AI. For example, use manual spreadsheets, paper-based records, or older software applications.

Adjust Expectations: Understand that there may be a temporary decrease in efficiency and productivity without AI. Adjust expectations and communicate this to stakeholders.

Regular Updates and Monitoring: Continuously monitor the status of your AI systems and work on their recovery. Keep stakeholders informed about progress toward returning to normal operations.

Collaboration: Collaborate with other organizations or partners that may have similar AI dependencies. Sharing resources and knowledge can help everyone navigate the temporary AI outage.

Evaluate Alternatives: Use the downtime as an opportunity to explore alternative AI solutions or diversify your technology stack to reduce reliance on a single AI system.

Recovery Plan: Develop a clear plan for recovering AI systems and returning to normal operations. This should include testing and validation procedures to ensure the AI is safe to use.

Remember that living without AI temporarily may require patience and adaptability, but with careful planning and communication, it's possible to maintain business continuity until the AI systems are back to normal operation.