AI Security

8 automated security scanners

AI System Privacy

Purpose: The AI_System_Privacy Scanner is designed to detect and mitigate model inversion and membership inference vulnerabilities in AI systems. By identifying patterns indicative of these attacks, the scanner helps safeguard sensitive data and ensures compliance with privacy and security policies, thereby protecting user privacy and organizational security.

What It Detects:

Model Inversion Patterns: Identifies instances where attackers might infer input data from model outputs through specific phrases such as “model inversion attack,” “input reconstruction,” or “data leakage.”
Membership Inference Patterns: Recognizes signs of membership inference attacks, which involve determining whether a particular data point was part of the training dataset using keywords like “membership inference,” “training set inclusion,” or “data presence detection.”
Data Protection Policies: Analyzes company security documentation and policies to ensure they address risks associated with model inversion and membership inference. Relevant policy indicators include “security policy,” “incident response,” “data protection,” and “access control.”
Compliance Certifications: Verifies if the organization holds relevant compliance certifications that address data privacy and security, such as SOC 2 or ISO 27001. Indicators of maturity in this area include mentions of these certifications and related practices like penetration testing and vulnerability scans.
Trust Center Information: Examines trust center content to ensure transparency and robust security measures are in place regarding data protection and access control.

Inputs Required:

domain (string): The primary domain to analyze, e.g., “acme.com.” This parameter helps the scanner identify relevant information across the company’s website.
company_name (string): The name of the company for which the analysis is being conducted, such as “Acme Corporation.” This assists in searching and identifying specific content related to the organization.

Business Impact: Ensuring that sensitive data remains protected from unauthorized exposure through AI model outputs is crucial for maintaining user trust and compliance with regulatory standards. The scanner’s ability to detect vulnerabilities early helps mitigate potential risks associated with privacy breaches, which can significantly impact an organization’s security posture and reputation.

Risk Levels:

Critical: Conditions that directly lead to significant data exposure or immediate non-compliance with critical regulations are considered critical. This includes scenarios where specific keywords related to model inversion or membership inference are detected without appropriate mitigation measures in place.
High: Conditions that pose a high risk of unauthorized data access but do not necessarily violate strict regulatory requirements are classified as high. These include instances where the scanner identifies potential risks without clear evidence of exposure.
Medium: Conditions that indicate moderate risk, potentially requiring attention to improve security posture, are considered medium. This includes situations where policies or technical mitigations could be strengthened.
Low: Informational findings that do not pose significant risk but can still benefit from improvements in documentation and practices are classified as low. These include general mentions of data protection without specific actionable insights.
Info: Any findings that provide basic awareness about the scanner’s capabilities or compliance status, which does not directly affect security posture, are considered informational.

Example Findings:

“We detected an instance of model inversion attack where sensitive customer information was potentially exposed through model outputs.”
“Our investigation revealed potential membership inference risks in our AI training dataset that need immediate attention to comply with GDPR regulations.”

Adversarial Example Robustness

Purpose: The Adversarial Example Robustness Scanner is designed to assess and ensure the resilience of an organization’s security measures against adversarial examples. It aims to detect changes in attack resistance, evaluate the consistency of defensive measures, identify emerging vulnerabilities, review company policies for compliance, and analyze public information regarding security practices.

What It Detects:

Attack Resilience Changes: Identifies shifts in how attacks are handled or described, detects changes in incident response strategies, and monitors updates in security protocols that may indicate new vulnerabilities.
Defense Effectiveness Decay: Evaluates the consistency of defensive measures across different incidents, checks for signs of reduced effectiveness in existing security controls, and identifies patterns suggesting a decline in overall defense capability.
Vulnerability Emergence: Scans for mentions of newly discovered or emerging vulnerabilities, detects references to recent security patches or updates, and monitors indications of new attack vectors or threat models.
Policy Review and Compliance: Examines company security documentation for adherence to best practices, checks for compliance with relevant standards (e.g., SOC 2, ISO 27001), and verifies the presence of penetration testing and vulnerability assessment reports.
Public Information Analysis: Analyzes public policy pages and trust center information for transparency, looks for mentions of security incidents and how they were addressed, and evaluates the overall tone and content of security-related communications.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com) - This input is essential for scanning various paths on the company’s website to gather relevant security documentation and policy pages.
company_name (string): Company name for statement searching (e.g., “Acme Corporation”) - Used in search queries to identify specific information related to the organization’s cybersecurity practices.

Business Impact: This scanner is crucial as it helps organizations maintain a robust security posture against evolving threats, ensuring that their defenses remain resilient and effective over time. By proactively identifying vulnerabilities and changes in attack resistance, the scanner aids in making informed decisions about updating security measures and protocols to mitigate potential risks effectively.

Risk Levels:

Critical: The risk is critical if there are significant shifts in how attacks are described or handled without corresponding updates to security measures, indicating a possible immediate vulnerability that could lead to severe consequences.
High: A high risk level is indicated by consistent ineffective defensive strategies across incidents, suggesting potential weaknesses that could be exploited by adversaries with higher capabilities.
Medium: Medium risks pertain to less significant vulnerabilities or areas where defense effectiveness shows signs of decline but does not yet pose a critical threat.
Low: Low risks involve minimal changes in attack descriptions and generally effective defensive strategies, indicating a lower vulnerability to immediate threats.
Info: Informational findings are those that do not necessarily indicate an urgent risk but still suggest areas for improvement or transparency in security practices.

Example Findings:

The scanner might flag “Recent updates to the incident response plan do not reflect improvements in handling advanced persistent threat scenarios, suggesting a lack of adaptation to new threats.”
Another example could be “No recent mentions of compliance audits or vulnerability assessments have been documented, which may indicate outdated security measures and potential exposure to risks.”

Model Backdoor Vulnerability

Purpose: The Model Backdoor Vulnerability Scanner is designed to detect and assess potential vulnerabilities in machine learning models that could be exploited by malicious actors. It aims to identify susceptibility to trigger activation, manipulation vulnerabilities, and evaluate resilience against data poisoning in these models. This scanner helps organizations identify security weaknesses that may allow for model manipulation or degradation, ensuring the integrity and reliability of AI-driven systems.

What It Detects:

Trigger Susceptibility: Identifies hidden triggers within a machine learning model that could activate under specific conditions, potentially indicating backdoor mechanisms or unintended activation phrases.
Manipulation Vulnerability: Detects vulnerabilities in the model that allow for manipulation through adversarial inputs or other methods, highlighting potential data poisoning and input perturbation risks.
Poisoning Resilience: Evaluates the model’s ability to withstand poisoned training data by examining its detection mechanisms such as anomaly detection and ensemble methods.
Security Documentation Review: Analyzes company security documentation for policies related to AI and machine learning model security, including compliance with standards like SOC 2 and ISO 27001.
Public Policy Pages and Trust Center Information: Scrapes public policy pages and trust center statements for information on AI security practices and incident response plans.

Inputs Required:

domain (string): The primary domain of the organization to be analyzed, providing a context for scanning across various online resources.
company_name (string): The name of the company or entity being investigated, used in search queries to gather relevant security and policy statements.

Business Impact: This scanner is crucial for organizations operating with AI-driven systems as it helps identify potential backdoors and vulnerabilities that could be exploited by malicious actors, thereby safeguarding critical business operations and intellectual property against unauthorized manipulation.

Risk Levels:

Critical: Conditions where the model exhibits clear signs of hidden triggers or susceptibility to data poisoning without any mitigation strategies in place.
High: Situations where there are indications of vulnerability but with some level of protection mechanisms, posing a significant risk if exploited.
Medium: Models that show resilience against basic manipulation but lack robust defenses against advanced attacks, requiring further enhancement for comprehensive security.
Low: Informational findings indicating minimal exposure to backdoor vulnerabilities or data poisoning risks, generally considered secure under normal operational conditions.
Info: Non-critical findings related to compliance with general AI and machine learning security standards without specific evidence of vulnerability.

Risk levels are inferred based on the potential impact of each detection point and the overall risk posed by these vulnerabilities in a typical deployment scenario.

Example Findings:

The model exhibits activation phrases that could be exploited to trigger unintended behavior under certain conditions, indicating susceptibility to backdoor attacks.
There is no mention of data validation or sanitization processes, which could lead to significant risk if the model’s training data becomes poisoned.

Privacy Protection Degradation

Purpose: The Privacy Protection Degradation Scanner is designed to identify potential data leakage, membership inference vulnerabilities, and model inversion risks by analyzing company security documentation, public policy pages, trust center information, and compliance certifications.

What It Detects:

Data Leakage Potential Indicators: Searches for patterns indicating inadequate data protection measures, identifies mentions of unsecured data storage or transmission methods, and looks for gaps in encryption practices and data handling policies.
Membership Inference Vulnerability Indicators: Detects language suggesting weak anonymization techniques, identifies references to insufficient user data obfuscation, and checks for indications of improper access controls leading to potential inference attacks.
Model Inversion Risk Indicators: Searches for patterns indicating inadequate model security measures, identifies mentions of unsecured training data or model parameters, and looks for gaps in differential privacy practices and model deployment policies.
Security Policy Compliance: Verifies the presence of comprehensive security policies, checks for references to compliance certifications like SOC 2, ISO 27001, and ensures that penetration testing and vulnerability assessments are mentioned.
Trust Center Information: Analyzes trust center pages for transparency in data handling practices, identifies mentions of incident response plans and breach disclosure procedures, and verifies the presence of user privacy policies and data protection measures.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial as it helps organizations assess their data handling practices and security measures against potential threats, ensuring compliance with regulations and enhancing user trust in the organization’s commitment to privacy and security.

Risk Levels:

Critical: Conditions that directly lead to significant data breaches or severe non-compliance with legal and regulatory requirements.
High: Conditions that pose a high risk of unauthorized access to sensitive information, potential data leakage, or significant compliance violations.
Medium: Conditions that indicate moderate risks such as inadequate encryption practices or incomplete security policies.
Low: Conditions that suggest minor issues like minor gaps in documentation or minor non-compliance with recommended practices.
Info: Informal findings indicating areas for improvement but currently not posing immediate threats.

Example Findings:

A company’s privacy policy lacks explicit mention of data encryption methods, which could be a medium risk as it potentially exposes the organization to higher data leakage risks.
The trust center does not include details on breach notification procedures, representing a low risk but still requiring attention for better transparency and user protection.

Model Access Control Integrity

Purpose: The Model Access Control Integrity Scanner evaluates the effectiveness of authorization enforcement, authentication strength, and API security measures by analyzing company documentation, public policies, trust center information, and compliance certifications. This ensures that organizations maintain robust access controls and secure APIs to protect sensitive data and prevent unauthorized access.

What It Detects:

Authorization Enforcement: Identifies gaps in role-based access control (RBAC) implementation, checks for proper segregation of duties and least privilege principles, verifies the presence of multi-factor authentication (MFA) requirements, and evaluates session management practices including timeouts and token expiration.
Authentication Strength: Assesses the strength of password policies, including complexity and length requirements, detects the use of weak or default credentials, reviews MFA implementation across different user roles, and ensures secure storage and handling of authentication tokens.
API Security: Identifies open APIs that lack proper authentication mechanisms, checks for sensitive data exposure in API responses, evaluates rate limiting and throttling measures to prevent abuse, and verifies the use of HTTPS and secure communication protocols.
Policy Indicators: Searches for security policy documents mentioning “access control,” “data protection,” “incident response,” and other relevant terms, validates the presence of comprehensive security policies that address access management and API security.
Maturity Indicators: Looks for compliance certifications such as SOC 2, ISO 27001, and penetration test results, checks for evidence of regular vulnerability assessments and scans, evaluates the maturity of security practices based on documented processes and procedures.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial as it helps organizations maintain robust access controls and secure APIs, which are essential for protecting sensitive data from unauthorized access. Inefficient authorization enforcement can lead to data breaches, while weak authentication mechanisms can be exploited by malicious actors. Ensuring strong API security practices prevents data leakage and abuse of services.

Risk Levels:

Critical: Conditions that directly compromise the security posture, such as lack of MFA implementation across all roles or exposure of sensitive data through APIs without proper encryption.
High: Conditions that significantly increase risk but do not fully compromise security, such as incomplete documentation around access controls and authentication practices.
Medium: Conditions that indicate potential risks if left unaddressed, like the use of weak passwords in policies.
Low: Informative findings that suggest areas for improvement but do not pose immediate threats, such as minor discrepancies in password complexity requirements.
Info: General recommendations to enhance security posture or clarify existing practices, which are useful but may not be critical.

Example Findings:

The company lacks a detailed incident response plan covering all possible scenarios that could impact data integrity and availability.
Some user roles do not enforce MFA, increasing the risk of unauthorized access through compromised credentials.
Sensitive information is exposed in API responses without encryption, which can lead to unauthorized data leakage when intercepted over networks.

Model Supply Chain Security

Purpose: The Model Supply Chain Security Scanner is designed to identify potential security issues in pre-trained components and ensure the integrity of transfer learning sources by analyzing company security documentation, public policy pages, trust center information, and compliance certifications.

What It Detects:

Security Policy Indicators: Identifies the presence or absence of key security policies such as “security policy,” “incident response,” “data protection,” and “access control.”
Maturity Indicators: Evaluates the maturity of security practices by looking for compliance certifications like SOC 2, ISO 27001, penetration testing, and vulnerability scanning.
Supply Chain Attack Framing: Detects mentions of supply chain attacks or vulnerabilities that could indicate potential security risks in third-party components.
Vendor Risk Management: Checks for statements related to vendor risk management practices, ensuring that companies are actively managing their third-party relationships.
Compliance Certifications: Verifies the presence of compliance certifications and standards that demonstrate a company’s commitment to maintaining high security standards.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
company_name (string): Company name for statement searching (e.g., “Acme Corporation”)

Business Impact: This scanner is crucial for assessing the security posture of organizations by identifying gaps in their security policies and practices, which can lead to improved risk management and compliance with industry standards.

Risk Levels:

Critical: Conditions that directly impact critical systems or where failure could result in significant harm (e.g., unauthorized access to sensitive data).
High: Conditions that have a high potential for negative consequences if not mitigated, such as severe privacy violations or system unavailability.
Medium: Conditions that may lead to moderate risks if left unchecked, affecting the efficiency of operations or requiring immediate attention.
Low: Informal or non-critical findings that do not pose significant risk but could be improved for better security practices.
Info: General information that does not directly affect security posture but provides insights for continuous improvement.

Example Findings:

A company lacks a comprehensive “security policy” document, which is critical for establishing baseline security measures.
The organization has outdated compliance certifications like ISO 27001, indicating potential lapses in maintaining up-to-date security standards.

ML Model Security

Purpose: The ML_Model_Security Scanner is designed to safeguard AI models by identifying potential threats such as adversarial examples, model poisoning, and data extraction attempts. It evaluates company documentation, public policy pages, trust center information, and compliance certifications to ensure robust security measures are in place against these risks.

What It Detects:

Adversarial Example Indicators: The scanner identifies mentions of adversarial attacks or inputs intended to deceive models, descriptions of model vulnerabilities related to such examples, the presence of mitigation strategies, and vague security statements lacking specific defenses against adversarial examples.
Model Poisoning Patterns: This includes detecting data tampering or malicious input during training, indications of supply chain attacks on model data, mechanisms for detecting poisoned datasets, and transparency issues in handling and validation processes.
Data Extraction Attempts: The scanner flags unauthorized access to sensitive data, mentions of data exfiltration, and the effectiveness of prevention measures including encryption and secure storage practices.
Security Policy Indicators: It highlights explicit security policies relevant to AI model protection, incident response plans addressing AI threats, specific data protection measures for machine learning models, and comprehensive access control protocols.
Compliance and Maturity Indicators: The scanner looks for SOC 2 compliance certifications, adherence to ISO 27001 standards, regular penetration testing, and vulnerability assessments to ensure high security standards are maintained.

Inputs Required:

domain (string): Primary domain of the company website to be analyzed.
company_name (string): The name of the company for which the analysis is conducted.

Business Impact: Ensuring robust AI model security measures is crucial as it directly impacts data integrity, operational resilience, and compliance with regulatory standards. Poorly secured models can lead to unauthorized access, data breaches, and significant financial losses, impacting both business operations and customer trust.

Risk Levels:

Critical: Findings that indicate a direct vulnerability or imminent threat to the AI model’s security without mitigation strategies in place.
High: Significant risks requiring immediate attention due to high exposure of sensitive data or critical functionalities at risk.
Medium: Risks where improvements are recommended but not immediately critical, such as gaps in policy enforcement or incomplete detection mechanisms.
Low: Informal findings that suggest minor issues which can be addressed over time with planned enhancements.
Info: Non-critical information that does not directly impact security but could be useful for continuous improvement and transparency.

Example Findings:

“The company’s privacy policy lacks explicit mention of data encryption during transmission or storage, posing a medium risk.”
“There are no documented procedures to detect or prevent model poisoning attacks; this is considered critical.”

AI Supply Chain

Purpose: The AI Supply Chain Scanner is a tool designed to detect potential issues such as training data poisoning and pre-trained model backdoors within an organization’s machine learning supply chain. This ensures that the models used by the company are secure, compliant with security policies, and free from malicious tampering.

What It Detects:

Training Data Poisoning Indicators: The scanner identifies suspicious patterns in training datasets which may suggest intentional manipulation of data to mislead model performance or outcomes.
Pre-trained Model Backdoors: It scans the models’ weights and architectures for any hidden malicious code or triggers that could be activated under specific conditions, potentially compromising the integrity of the model.
Security Policy Compliance: The scanner checks whether the organization adheres to internal policies related to AI supply chain management, including data protection measures and incident response plans tailored for AI threats.
Vendor Risk Management: It evaluates third-party vendors’ documentation for compliance with security standards and reviews contractual terms concerning model integrity and security.
Access Control Measures: The scanner examines the access controls governing interactions with sensitive training data and models, ensuring robust authentication and authorization protocols are in place to prevent unauthorized use or manipulation.

Inputs Required:

domain (string): This input specifies the primary domain of interest for analysis, such as a company’s website address.
company_name (string): The name of the company is used during statement searching to gather relevant information about its security practices and policies.

Business Impact: Ensuring the integrity and security of machine learning models used by organizations is crucial for maintaining trust in AI applications, avoiding financial losses due to model manipulation, and complying with increasingly stringent data protection regulations.

Risk Levels:

Critical: Conditions that directly lead to significant vulnerabilities or breaches that could result in substantial damage or unauthorized access are considered critical.
High: High-risk scenarios involve potential risks of severe impact on operations if not addressed promptly.
Medium: These are moderately serious issues that require attention but do not pose an immediate threat as those at the high risk level.
Low: Generally less concerning, these findings may still need monitoring or improvement but generally do not affect critical functions immediately.
Info: Informal and mostly advisory in nature, these findings provide additional context without directly impacting security posture significantly.

If specific conditions for each risk level are not detailed in the README, they should be inferred based on the overall purpose of the scanner and its potential impacts.

Example Findings: The scanner might flag a dataset with unusually high outlier values or detect an unexplained spike in model errors under certain inputs indicative of a backdoor.