LLM Feedback Loop Security

5 automated security scanners

Red Team Feedback Capture

Purpose: The Red Team Feedback Capture Scanner is designed to identify potential data exfiltration attempts, exposure of vulnerability documentation, and leakage of attack patterns by analyzing various threat intelligence feeds and domain-specific data.

What It Detects:

Testing Data Exfiltration Indicators: Patterns indicating data exfiltration such as “exposed|leaked|breached” or “unauthorized\s+access”.
Vulnerability Documentation Exposure: Detection of known vulnerabilities using CVE identifiers like “CVE-[0-9]{4}-[0-9]+”.
Attack Pattern Leakage: Identification of attack patterns such as “malware|ransomware|trojan” or related terms.

Inputs Required:

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

Business Impact: This scanner is crucial for organizations aiming to protect their sensitive information and comply with regulatory standards such as GDPR, HIPAA, and PCI DSS by detecting potential security breaches and unauthorized access attempts.

Risk Levels:

Critical: Conditions that directly lead to significant data loss or exposure of critical systems are considered critical.
High: Conditions indicating high risk of data leakage or system compromise are classified as high.
Medium: Conditions suggesting moderate risk but still requiring attention are categorized as medium.
Low: Informal findings that do not pose immediate threats, but should be monitored for future developments.
Info: General information about the domain’s security posture and potential improvements.

If specific conditions for each risk level are not detailed in the README, consider them based on general cybersecurity principles.

Example Findings:

Detection of unauthorized access attempts in exposed data from a company website.
Identification of CVE-2021-44228 (Apache Log4j vulnerability) documented but potentially unpatched on the target domain’s systems.

Model Behavior Drift

Purpose: The Model Behavior Drift Scanner is designed to detect incremental training shifts, subtle preference changes, and performance degradation in machine learning models by analyzing domain-specific data and comparing it against historical patterns. This tool helps organizations maintain the integrity and reliability of their AI systems by identifying deviations that may indicate model drift or suboptimal performance.

What It Detects:

Incremental Training Shifts: Identifies changes in model behavior due to new training data, which can affect accuracy and predictive power.
- Real Pattern Example: “Recent updates have improved our model’s accuracy.”
Subtle Preference Changes: Detects shifts in model preferences or biases over time, which could lead to unfair treatment of certain user groups.
- Real Pattern Example: “The system now prioritizes user feedback more heavily.”
Performance Degradation: Monitors for any decline in model performance metrics, indicating a potential loss of effectiveness.
- Real Pattern Example: “We have noticed a slight drop in response times.”
Anomaly Detection in Predictions: Identifies unusual or unexpected predictions from the model, which may require further investigation to ensure reliability and correctness.
- Real Pattern Example: “The model predicted an outlier value that requires investigation.”
Historical Comparison: Compares current model behavior against historical baselines to identify drifts, helping in understanding long-term performance trends.
- Real Pattern Example: “Compared to previous versions, the model’s error rate has increased by 2%.”

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com). This input is crucial for directing the scanner to the relevant data sources and ensuring accurate analysis of model behavior across different domains.

Business Impact: Monitoring model behavior drift is essential for maintaining a secure and reliable AI posture, as it helps in proactively addressing issues that could lead to significant performance degradation or security vulnerabilities.

Risk Levels:

Critical: Conditions where there are abrupt changes in model predictions with no apparent change in input data, which can indicate critical system failures or potential malicious activities.
High: Significant deviations in model behavior metrics that might affect key business processes and user trust, such as a substantial drop in accuracy without clear explanation.
Medium: Minor shifts in model behavior that could lead to subtle preference changes or reduced performance efficiency but do not critically impact the system’s functionality.
Low: Minimal drift observed with no apparent negative effects on model performance or user experience, considered within normal operational variability.
Info: Routine findings of incremental updates and minor adjustments in model training data that are part of routine maintenance activities.

Example Findings:

“Recent updates have improved our model’s accuracy.”
“We have noticed a slight drop in response times.”

Fine-tuning Data Security

Purpose: The Fine-tuning Data Security Scanner is designed to identify and mitigate potential risks associated with dataset contamination, adversarial examples, and prompt injection persistence in fine-tuned models. By analyzing training datasets for unauthorized modifications, detecting adversarial attacks, and monitoring for persistent prompt injections, this tool aims to safeguard machine learning models against malicious activities and data breaches.

What It Detects:

Dataset Contamination: Identifies patterns indicative of data tampering or unauthorized modifications within the training datasets, which could lead to compromised model performance and security.
Adversarial Examples: Detects signs of adversarial attacks that aim to deceive machine learning models into making incorrect predictions, compromising the integrity and reliability of the models.
Prompt Injection Persistence: Monitors for persistent prompt injection attempts that could result in unauthorized access or manipulation of the model’s behavior, posing significant security risks.
Threat Indicators from APIs: Utilizes threat intelligence feeds to identify known vulnerabilities, malware signatures, and other malicious activities associated with the domain, providing proactive defense against emerging threats.
Exposure Indicators: Identifies patterns suggesting data exposure or unauthorized access, which are critical for ensuring that sensitive information remains protected within the model’s environment.

Inputs Required:

domain (string): The primary domain to analyze, serving as the focal point for threat and exposure indicator analysis.

Business Impact: This scanner is crucial for maintaining the security and integrity of fine-tuned models used in critical applications such as financial services, healthcare, and government systems. Detecting and mitigating dataset contamination, adversarial examples, and prompt injection persistence helps prevent data breaches and ensures that sensitive information remains protected from unauthorized access.

Risk Levels:

Critical: Conditions that directly lead to severe security vulnerabilities or significant data exposure are considered critical risks. These include patterns of tampering with training datasets and persistent attempts at prompt injection that bypass authentication mechanisms.
High: Risks involving the detection of malware signatures, unauthorized access patterns, and potential command and control (C&C) activities are classified as high severity. These threats could lead to significant data breaches or system manipulation if not promptly addressed.
Medium: Medium risk conditions involve exposure indicators such as leaked data or indications of compromised authentication mechanisms. While less severe than critical risks, these conditions still pose a significant threat and require immediate attention for mitigation.
Low: Informational findings related to benign network activities or minimal exposure are considered low severity. These findings typically do not pose an immediate risk but should be monitored for any changes in behavior that might indicate increased vulnerability.
Info: Any findings that do not meet the criteria for high, medium, or low risks are categorized as informational. These include general indicators of network activity and minimal data exposure scenarios.

Example Findings:

A pattern indicative of tampering with a training dataset is detected, which could lead to unauthorized modifications affecting model predictions.
Significant evidence of malware presence in the system environment, potentially compromising the integrity of fine-tuned models used for critical applications.

RLHF Exploitation

Purpose: The RLHF Exploitation Scanner is designed to identify and detect potential malicious activities in reinforcement learning systems that use human feedback (RLHF). This includes detecting manipulation of reward models, influence on human evaluators, and preference optimization attacks aimed at skewing the outcomes of these systems.

What It Detects:

Reward Model Manipulation Indicators: Detection of suspiciously high-frequency requests to specific endpoints that may indicate attempts to manipulate the outcome of RLHF systems by influencing how rewards are calculated.
Human Evaluator Influence Patterns: Analysis of unusual spikes in evaluator activity during off-hours or holidays, as well as detection of IP addresses associated with known malicious actors accessing evaluation interfaces, which could suggest influence or manipulation of human feedback.
Preference Optimization Attacks: Pattern matching for keywords related to optimization, such as “maximize,” “minimize,” or “optimize” in feedback comments, indicating potential automated tuning of preferences that could lead to biased outcomes.
Anomalous API Usage: Monitoring for unusual API call volumes or unexpected request parameters, which may suggest attempts at exploiting the system through unauthorized access or manipulation of data flows.
Malicious Content Indicators: Detection of known malicious content signatures using regular expressions in feedback comments and analysis of links to blacklisted domains or URLs associated with malware, ransomware, or trojans, indicating potential security breaches or malicious activities.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com). This is essential for gathering data from Shodan and VirusTotal APIs to assess the network and domain reputation of the specified domain.

Business Impact: The detection and mitigation of these malicious activities are crucial as they can lead to significant security breaches, biased decision-making in RLHF systems, and potential exploitation of sensitive user information. This directly impacts the integrity and reliability of AI applications that rely on human feedback for training and improvement.

Risk Levels:

Critical: Conditions where there is clear evidence of reward model manipulation or preference optimization attacks affecting critical system functions.
High: Conditions indicating significant influence on evaluators, such as unusual spikes in activity from known malicious actors during off-hours, which could lead to biased feedback and potentially harmful outcomes.
Medium: Conditions suggesting potential exposure to malicious content or anomalous API usage patterns that might indicate ongoing reconnaissance or preparatory stages of an attack.
Low: Informal findings related to minor anomalies in network traffic or minimal deviation from typical user behavior indicative of benign, though suspicious activity.
Info: Non-critical observations such as the presence of known malicious content signatures but without clear evidence of exploitation or influence.

Example Findings:

“Suspiciously high-frequency requests detected to endpoints that are typically used for feedback aggregation.”
“Unusual spike in evaluator activity during off-hours, indicative of potential manual intervention not aligned with typical user behavior.”

Analyst Feedback Poisoning

Purpose: The Analyst Feedback Poisoning Scanner is designed to detect training data contamination, model behavior manipulation, and feedback-based attacks by analyzing domain-specific threat intelligence feeds. It aims to identify potential security vulnerabilities and malicious activities related to known vulnerabilities, malware, ransomware, command and control (C2) activities, phishing, credential harvesting, data breaches, unauthorized access, and data dumps.

What It Detects:

Threat Indicators in Domain Reputation:
- Detection of CVE identifiers such as patterns like CVE-[0-9]{4}-[0-9]+ to identify known vulnerabilities.
- Identification of malware, ransomware, trojan horses, command and control (C2) activities, phishing, and credential harvesting through keywords and phrases.
Exposure Indicators:
- Detection of data breaches, leaks, unauthorized access, and data dumps using specific keywords and phrases.

Inputs Required:

domain (string): The primary domain to analyze, such as acme.com, which is used for collecting threat intelligence data from various sources including Shodan API, VirusTotal API, CISA KEV list, AbuseIPDB, and NVD/CVE database lookup.

Business Impact: This scanner is crucial for maintaining the integrity of training datasets in machine learning models, preventing manipulation of model outputs through poisoned feedback, and safeguarding against malicious activities that could compromise sensitive information or lead to unauthorized access.

Risk Levels:

Critical: The risk level is critical when known vulnerabilities are exploited without proper mitigation measures in place. This includes situations where the scanner identifies CVE identifiers matching those on the CISA KEV list, indicating active exploitation of a publicly disclosed vulnerability.
High: High risks are associated with domains that exhibit indicators of malware, ransomware, unauthorized access, and data breaches. These scenarios pose significant threats to cybersecurity posture and require immediate attention.
Medium: Medium risk is assigned when the scanner detects less severe vulnerabilities or exposure indicators such as potential phishing activities or limited unauthorized access attempts. This level requires monitoring and possibly remediation actions to prevent escalation into higher risks.
Low: Informational findings at the low risk level include general exposure to data breaches, which while concerning, may not necessarily indicate active exploitation without additional context. These should be monitored for trends but are less urgent than critical issues.
Info: This category includes purely informational findings that do not directly impact security posture but might require tracking or awareness raising within an organization.

Example Findings:

The scanner might flag a domain with multiple CVE identifiers as critical, indicating active exploitation of known vulnerabilities.
A high risk might be assigned to a site showing signs of malware and unauthorized access attempts, suggesting potential data theft or manipulation by malicious actors.