LLM Data Security

5 automated security scanners

Model Inversion Attacks

Purpose: The Model Inversion Attacks Scanner is designed to safeguard sensitive training data by detecting unauthorized access and leaks through the analysis of domain-specific threat intelligence feeds. It identifies patterns indicative of both training data reconstruction attempts and membership inference attacks, ensuring the security and privacy of the data are not compromised.

What It Detects:

Training Data Reconstruction Indicators: Patterns indicating attempts to reconstruct training datasets or unauthorized access related to training data.
Membership Inference Attack Signs: Identification of indicators suggesting attackers infer whether specific data points were part of the training dataset.
Threat Intelligence Feed Analysis: Utilization of Shodan API for exposed services and vulnerabilities, VirusTotal API for domain/IP reputation analysis, cross-referencing with CISA KEV for known exploited vulnerabilities, IP reputation checks using AbuseIPDB, and vulnerability lookup via NVD/CVE database.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)

Business Impact: This scanner is crucial in preventing data breaches that could lead to significant financial losses, legal repercussions, and damage to the organization’s reputation. It helps maintain trust between organizations and their customers by ensuring the confidentiality of sensitive information used for training machine learning models.

Risk Levels:

Critical: Conditions where there is clear evidence of unauthorized access or data leaks directly related to the training dataset.
High: Indicators of potential membership inference attacks, suggesting that specific data points might be at risk.
Medium: Vulnerabilities identified through threat intelligence feeds but not yet exploited or breached.
Low: Informational findings indicating exposure without concrete evidence of unauthorized access or data breach.
Info: General exposure indicators such as leaked keywords found in the domain’s public records.

Example Findings:

A pattern matching CVE-2023-1234 indicative of a known vulnerability affecting multiple systems, suggesting potential exploitation by attackers.
An IP address associated with numerous malware-related domains and services, indicating exposure to various forms of cyber threats.

Training Data Leakage

Purpose: The Training Data Leakage Scanner is designed to identify and alert about the unauthorized extraction of personally identifiable information (PII) and intellectual property from training datasets, which could lead to significant privacy violations and potential misuse of sensitive data.

What It Detects:

1. PII Extraction Indicators: The scanner identifies patterns indicative of personal information such as names, email addresses, phone numbers, and social security numbers.
2. Intellectual Property Extraction Indicators: It detects indicators of intellectual property like trade secrets, patents, and copyrighted material through specific keyword patterns.
3. Threat Indicator Patterns: The scanner can identify known threat indicators from various sources including CVE entries, malware types, command and control (C&C) communications, and phishing activities.
4. Exposure Indicator Patterns: It detects signs of data exposure or breaches such as exposed personal information, unauthorized access attempts, and data dumps.
5. Domain-Specific Indicators: The scanner supports customizable detection for domain-specific PII and intellectual property indicators tailored to different sectors like financial, medical, or legal domains.

Inputs Required:

domain (string): Primary domain to analyze, which is crucial for the scope of data collection and analysis.

Business Impact: This scanner is critical as it helps in preventing potential privacy violations and unauthorized use of sensitive information that could lead to severe consequences such as legal penalties, financial loss, or reputational damage. It plays a pivotal role in maintaining the integrity and security of training datasets used for machine learning and AI development.

Risk Levels:

Critical: Findings include patterns directly linked to high-value intellectual property or personal information that could lead to immediate regulatory compliance issues or legal consequences.
High: Patterns indicate significant exposure of sensitive data, which if exploited, could have severe financial or reputational impacts.
Medium: The findings suggest a moderate risk level, typically involving patterns that might be less critical but still require attention for mitigation.
Low: Informational findings indicating minimal risk unless there are specific contextual indicators suggesting potential issues.
Info: These are generally non-critical findings and do not pose immediate risks but could be indicative of ongoing or future concerns needing monitoring.

Example Findings:

An email address pattern was detected in a dataset, which might indicate unauthorized data access or potential exposure of sensitive information.
A specific keyword associated with trade secrets was found within the training data, suggesting possible intellectual property theft.

Adversarial Examples

Purpose: The Adversarial Examples Scanner is designed to detect classification manipulation and output steering in machine learning models by identifying potential adversarial examples that could deceive the model into making incorrect predictions. This tool helps organizations ensure the integrity and reliability of their AI systems, safeguarding against malicious inputs that could lead to compromised decision-making processes.

What It Detects:

Classification Manipulation Indicators: The scanner identifies test patterns indicative of input perturbation designed to mislead classifiers, subtle modifications in data inputs leading to unexpected classifications, and the presence of known adversarial attack techniques such as Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD).
Output Steering Patterns: It detects attempts to steer model outputs towards specific classes by manipulating inputs, identifies patterns where small changes in input result in significant shifts in predicted class labels, and flags instances of unusually high confidence for incorrect predictions.
Threat Intelligence Indicators: The scanner searches for known vulnerabilities and exploits that could be used in adversarial attacks, checks for indicators of malicious activity or compromised systems related to the target domain, and verifies the presence of Common Vulnerabilities and Exposures (CVEs), malware signatures, and other threat intelligence feeds.
Exposure Indicators: It identifies patterns suggesting data exposure or breaches that could facilitate adversarial attacks, detects mentions of unauthorized access, data leaks, or security incidents, and flags indicators of compromised systems or services potentially exploited for adversarial purposes.

Inputs Required:

domain (string): The primary domain to analyze, providing the context for which the scanner will detect potential adversarial examples and output steering patterns.

Business Impact: Detecting and mitigating adversarial examples is crucial for maintaining trust in machine learning models used in critical infrastructure such as financial services, healthcare, and government systems. By identifying and blocking malicious inputs, organizations can protect their AI-driven decision-making processes from manipulation and ensure the reliability of model outputs.

Risk Levels:

Critical: The scanner identifies specific patterns indicative of severe vulnerabilities or exploits that could be directly exploited for adversarial attacks, such as CVEs with high impact on system integrity.
High: The scanner detects potential malicious activities or compromised systems related to targeted adversarial attacks, indicating a significant risk of data exposure and unauthorized access.
Medium: The scanner identifies subtle modifications in input data that could lead to unexpected classifications or elevated confidence in incorrect predictions, posing a moderate risk if not addressed promptly.
Low: Informational findings suggesting potential vulnerabilities are detected but do not pose an immediate threat, requiring further investigation for actionable steps.
Info: Findings related to exposure indicators suggest possible data leaks or breaches that could be exploited by adversaries, providing baseline information for security enhancements and compliance audits.

If the README doesn’t specify exact risk levels, infer them based on the scanner’s purpose and impact.

Example Findings: The scanner might flag instances of known CVEs such as CVE-2021-44228 indicating potential security vulnerabilities in software systems, or detect malware signatures like “ransomware” suggesting a significant risk to data integrity.

Sensitive Content Generation

Purpose: The Sensitive Content Generation Scanner is designed to detect and analyze potential malicious activities and policy violations by examining domain content for indicators of exploit development, command and control server references, phishing activities, data breaches, unauthorized access, and other related issues.

What It Detects:

Detection of patterns indicative of exploit or malware development.
Identification of command and control (C2) server references.
Search for terms associated with phishing and credential harvesting.
Recognition of exposure indicators such as data breaches and unauthorized access.
Analysis of domain content for compliance with security policies and regulations.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com). This is the essential input that specifies the target domain for analysis.

Business Impact: The scanner plays a crucial role in enhancing an organization’s cybersecurity posture by proactively identifying potential threats and compliance issues, enabling swift mitigation strategies and policy adjustments to safeguard sensitive information and operational integrity.

Risk Levels:

Critical: Detection of specific malware strains or indicators that are known to be actively exploited (e.g., CVE identifiers).
High: Identification of command and control servers or significant exposure indicators such as data breaches (e.g., unauthorized access, leaked data).
Medium: Recognition of general phishing terms or compliance violations not explicitly critical but indicative of potential risks.
Low: Informational findings related to generic threat indicators that may require further investigation for context but do not pose immediate high risks.
Info: Findings that provide basic insights into the domain’s security posture without reaching the severity levels defined as critical or high.

Example Findings:

Detection of a CVE identifier such as “CVE-2023-1234” in code snippets, indicating known vulnerabilities potentially exploited for malicious purposes.
Identification of command and control (C2) server references within the analyzed domain content, suggesting potential ongoing malware operations or malicious activities.

Output Manipulation

Purpose: The Output Manipulation Scanner is designed to identify and detect various forms of misinformation, such as hallucination exploitation and misinformation injection, by analyzing domain data through threat intelligence feeds. This tool aims to ensure that the provided information is accurate and reliable by detecting patterns associated with common vulnerabilities (CVE), malware indicators, command and control (C2) indicators, phishing attempts, credential harvesting activities, data exposure, and unauthorized access.

What It Detects:

CVE Pattern Detection: Identifies mentions of Common Vulnerabilities and Exposures (CVE) patterns in the domain’s data.
Malware Indicators: Detects mentions of malware, ransomware, or trojans within the data.
Command and Control (C2) Indicators: Identifies references to command and control servers that may be involved in malicious activities.
Phishing and Credential Harvesting: Detects indications of phishing attempts or activities related to credential harvesting.
Exposure Indicators: Identifies indicators of data exposure, leaks, or breaches that could compromise sensitive information.

Inputs Required:

domain (string): The primary domain to be analyzed for potential misinformation and security vulnerabilities. This input is crucial as it determines the scope of the analysis.

Business Impact: Ensuring accurate and reliable information dissemination is paramount in maintaining trust and confidence within digital ecosystems. Misinformation can lead to significant disruptions, including financial losses, reputational damage, and compromised cybersecurity posture. The Output Manipulation Scanner plays a critical role in mitigating these risks by proactively detecting and alerting about potential misinformation and exploitation attempts.

Risk Levels:

Critical: Conditions that directly lead to severe vulnerabilities or imminent data breaches are considered critical. These include high-profile CVE patterns, direct mentions of malware, trojans, and unauthorized access to sensitive information.
High: High-risk indicators such as phishing activities and exposure of potentially sensitive data can be classified as high risk. These indicate a significant potential for harm or exploitation.
Medium: Medium risk findings involve less severe vulnerabilities that could still pose risks but are not as critical as those at higher levels. This includes general mentions of malware, unauthorized access without direct evidence of sensitive information exposure.
Low: Informational findings such as generic data exposure warnings can be considered low risk unless there is clear evidence linking them to specific high-risk activities.
Info: These are primarily for informational purposes and do not directly indicate severe risks or vulnerabilities.

Example Findings:

A domain hosting a webpage with multiple CVE patterns such as CVE-2023-1234 and CVE-2022-5678, indicating potential security vulnerabilities that could be exploited for malicious purposes.
Presence of malware keywords like “ransomware” or “trojan” in the domain’s metadata, suggesting a risk of data encryption or unauthorized access controlled by threat actors.