Specialized Attack Simulations

AI-LLM Jailbreak Testing

  • May 9, 2025
  • 0

Sherlocked Security – AI/LLM Jailbreak Testing

Assessing AI & LLMs for Robustness Against Jailbreaks and Malicious Input Manipulation

1. Statement of Work (SOW)

Service Name: AI/LLM Jailbreak Testing
Client Type: Organizations Using LLMs (ChatGPT, GPT-4, etc.), AI Developers, and Data Security Teams
Service Model: AI Model Penetration Testing + Input Manipulation + Jailbreak Simulation
Compliance Coverage: GDPR, SOC 2, NIST 800-53, ISO 27001

Testing Areas:

  • Jailbreak Attack Simulation on AI/LLM Models
  • Prompt Injection and Adversarial Input Testing
  • Data Privacy and Model Output Manipulation
  • AI Model Security Review (Data, Code, and Configuration)
  • Model Robustness Against Malicious Actors

2. Our Approach

[Model Discovery] → [Input Manipulation Techniques] → [Prompt Injection Attack Simulation] → [Malicious Behavior Triggering] → [Model Security Review] → [Jailbreak Attack Report Generation]

3. Methodology

[Model Access Review] → [Prompt Analysis] → [Injection Testing (SAST+DAST)] → [Behavior Manipulation Simulation] → [Data Leak Testing] → [Report Generation & Recommendations]

4. Deliverables to the Client

  1. Jailbreak Attack Simulation Report
  2. Prompt Injection and Adversarial Attack Test Results
  3. Model Output Review (Sensitive Data Leaks, Unintended Responses)
  4. Recommendations for Model Hardening and Secure Prompt Design
  5. Adversarial Input Mitigation Strategies
  6. Security & Compliance Gap Report for AI/LLM Implementation

5. What We Need from You (Client Requirements)

  • Access to AI/LLM models or API endpoints for testing (e.g., OpenAI, custom models)
  • Specific use cases or scenarios where the AI/LLM will be deployed
  • Information on model inputs and configurations
  • Security protocols and access controls for AI environments
  • NDA and scope confirmation

6. Tools & Technology Stack

  • Prompt Injection Testing Tools: Langchain, OpenAI API
  • Security Testing: Burp Suite, OWASP ZAP, Metasploit
  • Adversarial Input Simulation: CleverHans, Foolbox
  • Model Analysis Tools: LIME, SHAP
  • Data Leak Detection: DiffBlue, Private AI Tools
  • Code & Configuration Review: GitHub Actions, SonarQube, BlackBox AI

7. Engagement Lifecycle

1. Model Access Review → 2. Input & Prompt Configuration Analysis → 3. Injection & Jailbreak Attack Testing → 4. Malicious Behavior Simulation → 5. Security Vulnerability Report → 6. Mitigation Recommendations & Re-testing

8. Why Sherlocked Security?

Feature Sherlocked Advantage
Advanced Jailbreak Attack Simulation Detailed testing for prompt injection, behavior manipulation, and jailbreak bypass
Adversarial Testing Expertise In-depth adversarial model testing using industry-leading tools
Real-World Attack Scenarios Simulates complex attack vectors like data leaks and malicious input triggers
Customizable Testing Approach Tailored to your AI use cases and specific deployment environments
Compliance Assurance Testing ensures AI systems align with security and compliance requirements

9. Real-World Case Studies

AI Assistant Misuse Through Jailbreak Attack

Issue: An AI model intended for customer support was vulnerable to a jailbreak attack that enabled users to bypass content moderation filters.
Impact: Attackers exploited the model’s natural language processing capabilities to extract sensitive data and perform actions outside the scope of intended use.
Fix: Implemented strict input validation, restricted model access with a more robust authentication mechanism, and applied prompt hardening techniques to prevent unauthorized input manipulations.

Data Privacy Breach via Prompt Injection

Issue: A language model used in a financial advisory app inadvertently disclosed user data after a prompt injection attack.
Impact: Attackers managed to alter the model’s responses by manipulating input prompts, leading to a potential data leak.
Fix: Introduced tighter controls around user inputs, employed adversarial testing, and added validation mechanisms to prevent unauthorized responses.

10. SOP – Standard Operating Procedure

  1. Model Access Review

    • Review the AI/LLM model, data access permissions, and API endpoints for security vulnerabilities.
    • Identify areas of the model that might be susceptible to unauthorized prompt injections or malicious input manipulation.

  2. Prompt Analysis & Test Case Creation

    • Review how the model processes input prompts and identify potential weak points (e.g., ambiguous inputs, unsecured endpoints).
    • Create detailed test cases based on real-world attack scenarios and adversarial techniques.

  3. Prompt Injection & Jailbreak Simulation

    • Simulate various prompt injection attacks to bypass input validation and trigger unintended model behavior.
    • Focus on attacks that could lead to data leakage, model misuse, or manipulation of model outputs.

  4. Malicious Behavior Testing

    • Test how the model responds to adversarial inputs designed to exploit vulnerabilities, such as altering output or evading content moderation filters.
    • Monitor for unintended outputs, such as confidential data exposure or undesirable behavior triggering.

  5. Data Leak & Security Risk Detection

    • Assess whether the model inadvertently exposes private, confidential, or protected data through its output.
    • Evaluate security risks such as unintended outputs and potential information leakage that could impact the business.

  6. Generate Report & Recommendations

    • Document all identified vulnerabilities, jailbreak techniques used, and results of input manipulation.
    • Provide actionable recommendations for securing the model and preventing similar exploits in the future.

11. AI/LLM Jailbreak Testing Checklist

1. Model Access & Security Review

  • Access Points Identification:
    • Map all entry points to the AI/LLM (APIs, web interfaces, custom integrations).
    • Evaluate API rate limits, authentication mechanisms, and session handling for potential security gaps.
  • Authentication & Authorization:
    • Review and validate OAuth, API keys, and token-based authentication for securing API endpoints.
    • Implement Multi-Factor Authentication (MFA) for access to the model’s administrative interfaces.
    • Verify that user inputs are properly sanitized, and cross-site scripting (XSS) attacks are mitigated.
  • Model Data Segregation & Access Control:
    • Ensure that users and systems can only access data and model functionalities that are within their scope.
    • Conduct Role-Based Access Control (RBAC) audits to prevent privilege escalation or unauthorized data access.

2. Input & Prompt Configuration Analysis

  • Input Validation:
    • Review input validation mechanisms to ensure that malicious inputs cannot bypass security checks.
    • Implement strict input sanitization for all user-provided data to avoid prompt injection attacks.
    • Test for potential issues with JSON parsing, SQL injection within prompt contexts, and shell command injection.
  • Context Management:
    • Check if the model improperly retains session-based context or data from previous interactions that could be exploited in a jailbreak attack.
    • Test for the model’s handling of ambiguous or conflicting inputs and ensure it does not provide unintended access through prompt-based manipulation.
  • Prompt Injection Detection:
    • Evaluate the model for susceptibility to prompt injection attacks where a malicious input manipulates its behavior.
    • Test for situations where injecting specific phrases, characters, or commands might alter model outputs, e.g., changing user intent or command functions.

3. Prompt Injection & Jailbreak Simulation

  • Adversarial Input Generation:
    • Use adversarial input generation tools such as CleverHans and Foolbox to craft inputs designed to exploit prompt vulnerabilities.
    • Test both white-box and black-box adversarial attacks to identify weaknesses that attackers might use to manipulate model behavior.
  • Jailbreak Payloads:
    • Test for specific jailbreak payloads such as attempts to bypass content filters, access restricted information, or manipulate model behaviors.
    • Introduce prompt commands that attempt to override model instructions, such as forcing the model to ignore its training or moderation filters.
    • Attempt to trigger illegal operations, like shell execution or SQL injection, via indirect inputs (e.g., embedding shell commands within natural language text).
  • Injection via Multi-Prompt Attacks:
    • Experiment with chaining multiple prompts together to induce unexpected behavior by leveraging the model’s sequence context.
    • Test to see if inputs from one prompt can affect the response of subsequent ones, potentially leading to unintended outputs or control over the system.

4. Data Leak Testing

  • Data Extraction Testing:
    • Identify potential risks where the model might inadvertently disclose sensitive information or confidential data, even when not explicitly requested (e.g., PII, business data).
    • Test for data leakage vulnerabilities by attempting to extract information such as usernames, passwords, or other personally identifiable information (PII).
  • Audit Model Outputs for Inadvertent Data Exposure:
    • Review the model’s responses for unintended data exposure. For example, testing if answers include data related to other users or internal confidential knowledge.
    • Use LIME or SHAP to visualize and inspect the model’s decision-making process to see if any unintentional information is leaked.

5. Model Output Review & Risk Assessment

  • Output Integrity Assessment:
    • Ensure that the model output does not diverge from expected behavior based on the input prompts.
    • Audit model output for discrepancies in expected responses and assess how easily attackers can manipulate those outputs.
  • Evaluate Consistency with Policy & Security Guidelines:
    • Check that all model outputs align with predefined security policies (e.g., content moderation rules, legal compliance, privacy regulations).
    • Evaluate how model parameters, such as temperature or max token length, could influence output variability, potentially enabling exploits.
  • Testing for Contextual Leaks:
    • Ensure that no contextual information is leaking from previous interactions (e.g., through token/parameter history) that might provide an attacker insight into the AI’s prior inputs or state.

6. Jailbreak Prevention & Mitigation Measures

  • Prompt Hardening Techniques:
    • Implement prompt engineering techniques to strengthen model resistance to jailbreak attacks. Use structured, context-aware inputs to limit the scope for adversarial manipulation.
    • Add contextual rules to prevent prompt injection from bypassing security models.
  • Model Re-training:
    • Regularly retrain models using sanitized datasets and incorporate defensive measures, like adversarial examples, into the training pipeline.
    • Include adversarial robustness in model performance criteria, ensuring that models are explicitly tested for resistance against such attacks.
  • Response Filtering:
    • Implement response filtering techniques, such as post-processing or behavior sanitization, to clean outputs of any malicious instructions or dangerous behaviors.
    • Use content moderation tools (e.g., custom filters or pre-built libraries) to block certain input types from reaching the model.

7. Security Controls & Safeguards

  • Rate Limiting & Abuse Detection:
    • Implement rate limiting on APIs to prevent brute force or rapid input generation attacks.
    • Introduce abuse detection algorithms (such as anomaly detection) to detect unusual patterns of interaction that might indicate an attempt at model manipulation.
  • Audit Logging & Monitoring:
    • Enable detailed audit logging of all API calls, including the parameters used, timestamps, and the responses returned by the AI model.
    • Integrate monitoring systems to detect unusual model behavior, such as a spike in queries that attempt to bypass content filters or prompt security checks.
  • Incident Response Plan:
    • Develop an incident response plan for handling exploitation attempts, including a procedure for quickly identifying, mitigating, and patching jailbreak vulnerabilities.
    • Implement automated alerting in case of suspicious activity (e.g., sudden increases in malicious input, data extraction attempts).

8. Ongoing Security Measures

  • Continuous Testing & Validation:
    • Implement continuous integration pipelines for testing AI/LLM models regularly using adversarial input testing tools (e.g., Langchain, OpenAI API testing).
    • Schedule regular vulnerability scans on both the training dataset and deployed model to identify new potential exploits as adversarial techniques evolve.
  • Red Teaming Exercises:
    • Conduct Red Team exercises focusing specifically on bypassing model defenses and evaluating how attackers might manipulate the model in a production environment.
    • Test model security in real-world attack scenarios, integrating with phishing campaigns, social engineering, and exploitation attempts against the AI model.
Automated SBOM Generation
Credential Stuffing & ATO Simulation