AI Model Security

Extracted Attributes

• Key Goals

  Data protection, model integrity, preventing misuse, safeguarding intellectual property.

• Definition

  The field of protecting AI systems and their components (data, algorithms, models, infrastructure) from various security threats and vulnerabilities.

• Key Threats

  Adversarial attacks, data poisoning, unauthorized access, model theft (e.g., via distillation), membership inference attacks, attribute inference attacks, evasion attacks, content-bias injection, deepfakes.

• Mitigation Techniques

  Differential privacy frameworks, federated learning platforms, model encryption solutions, AI-based security monitoring systems, rigorous vetting of models and vendors, access controls (e.g., RBAC, MFA), encryption (at rest and in transit), watermarking models, bias audits, bias-correction techniques, robust sandboxing environments.

Timeline

• Anthropic introduced the Model Context Protocol (MCP), an open standard, open-source framework to standardize how AI systems integrate and share data with external tools, systems, and data sources. (Source: wikipedia)

  2024-11-01

Model Context Protocol

The Model Context Protocol (MCP) is an open standard, open-source framework introduced by Anthropic in November 2024 to standardize the way artificial intelligence (AI) systems like large language models (LLMs) integrate and share data with external tools, systems, and data sources. MCP provides a universal interface for reading files, executing functions, and handling contextual prompts. Following its announcement, the protocol was adopted by major AI providers, including OpenAI and Google DeepMind.

Web Search Results

• Top 14 AI Security Risks in 2024 - SentinelOne

  Some of the tools that can be used for securing AI models are differential privacy frameworks, federated learning platforms, model encryption solutions, and AI-based security monitoring systems such as SentinelOne. These services defend against multiple security threats and weaknesses. Discover More About Data and AI ------------------------------- ? [...] AI security is the field of protecting AI systems and their components from various security threats (e.g., adversarial attacks) and vulnerabilities (e.g., data poisoning). It means protecting the data, algorithms, models, and infrastructure involved in AI applications. The role of AI security is to make sure that the system is secure and working properly. These include everything from unauthorized access to data breaches and attacks on the AI that could compromise its functionality or outputs. [...] 1. Data Protection: Many AI systems deal with massive amounts of sensitive data. So, securing this data is necessary as it will help prevent a data breach. 2. Model Integrity: Tampering with malicious data could compromise the effectiveness of AI models. Thus, it is necessary to maintain the integrity of the model. 3. Preventing Misuse: AI security helps prevent attackers from exploiting AI systems for harmful purposes.

• Top 6 AI Security Risks and How to Defend Your Organization

  Adopting AI technologies requires rigorous vetting of models and vendors to ensure they meet security standards. This involves evaluating the security practices of third-party vendors and scrutinizing the design and implementation of AI models for potential vulnerabilities. By allowing only AI solutions that have passed security assessments, organizations can reduce the risk of introducing insecure components into their systems. [...] Primary concerns include adversarial attacks aimed at deceiving AI models, unauthorized data access leading to privacy breaches, manipulation of data to skew AI decisions (data poisoning), and theft of proprietary AI models. Addressing these risks requires a security strategy tailored to the challenges posed by AI. This article is part of a series about AI security. In this article [...] AI security risks include vulnerabilities and potential threats that arise from the use of artificial intelligence technologies. These risks can lead to unauthorized access, manipulation, or misuse of AI systems and data, or they might involve the use of AI technology to attack other systems. As AI models become more complex and widespread, the attack surface for malicious actors expands, making it crucial to understand and mitigate these risks.

• Securing AI Systems Against Cyber Threats | Exabeam

  Securing AI models against theft involves a combination of access controls, encryption, and potentially watermarking models to trace unauthorized use. Ensuring that models are protected both at rest and in transit is essential for safeguarding intellectual property and maintaining the integrity of AI systems. ### Tips from the expert Image 16: Steve Moore [...] Controlling access to AI models is essential to prevent unauthorized use and tampering. This means setting up strict access controls and authentication mechanisms to ensure only authorized personnel can interact with AI systems. [...] Implementing role-based access control (RBAC) and multi-factor authentication (MFA) can help in securing AI models against unauthorized access, providing an additional layer of security by verifying user identities and restricting access based on user roles and permissions. ### Secure the Code

• AI Security: Using AI Tools to Protect Your AI Systems - Wiz

  AI security is a key component of enterprise cybersecurity that focuses on defending AI infrastructure from cyberattacks. Focusing on AI security is vital because numerous AI technologies are woven into the fabric of organizations.AI is the engine behind modern development processes, workload automation, and big data analytics. It’s also increasingly becoming an integral component of many products and services. For example, a banking app provides financial services, but AI-powered technologies [...] Wiz also offers AI-security support for Amazon SageMaker and Vertex AI users that can help monitor and mitigate the security risks associated with managing AI/ML models. Wiz’s customized features for Vertex AI and Amazon SageMaker integrations include robust sandboxing environments, complete visibility across cloud applications, the safeguarding of AI pipelines, and agile deployment of ML models into production. Get a demo to explore how you can leverage the full capabilities of AI without [...] The process of gathering, processing, and storing data is fundamental in the domain of machine learning engineering. Integrating with model engineering tasks demands robust security protocols to protect data from breaches, intellectual property theft, supply chain attacks, and data manipulation or poisoning. Ensuring data integrity is pivotal in reducing both deliberate and accidental data discrepancies. ### Data poisoning

• 7 Serious AI Security Risks and How to Mitigate Them - Wiz

  Cybercriminals might attempt to determine if an AI model includes a specific individual’s data (membership inference attack) or analyze the model’s output to extract sensitive information (attribute inference attack). Generative AI (GenAI) applications, especially those built on large language models (LLMs), are particularly sensitive to these types of attacks, so it’s important to monitor GenAI security closely. Mitigation: Implement robust encryption for data at rest and in transit. [...] AI models can behave in unexpected ways in production, which can adversely affect user experience and open up the system to a variety of known and unknown threats. Real-life attack scenarios: Malicious actors might manipulate the model’s behavior by subtly altering the input data (evasion attack) or by strategically positioning and manipulating data during model training (data poisoning attack). Image 12 [...] An attacker could aim to create hyper-realistic fake content using your AI model to spread misinformation (deepfakes), or a malicious actor may try to inject bias into your model via input manipulation (content-bias injection). Mitigation: Conduct bias audits on training data and model outputs using tools like Fairness Indicators. Advocate for the implementation of bias-correction techniques, such as re-weighting or re-sampling, during model training.