Introduction

Background and Context of Agentic AI

This research is part of a joint initiative between the Cloud Security Alliance (CSA) and OWASP AI Exchange, building upon the previously published Agentic AI Red Teaming Guide. The objective of this study is to evaluate the capabilities and identify the gaps of existing tools in the context of agentic AI red teaming.

For this research, Microsoft’s Python Risk Identification Toolkit (PyRIT) was selected as the initial tool of analysis, given its open-source availability and its extensive use by Microsoft and other model developers for red teaming foundation models. This research focuses on examining PyRIT’s capabilities and effectiveness in supporting agentic AI red teaming.

The goal is to determine whether PyRIT can systematically prepare, execute, analyze, and report on attacks against agentic AI models, and to identify any gaps or enhancements needed for full support of such advanced testing.

For further background on the motivations, requirements, and methodology of agentic AI red teaming, please refer to the Agentic AI Red Teaming Guide.

Overview of PyRIT

PyRIT is an open-source framework developed by Microsoft and designed to address the evolving threat landscape of generative AI (GenAI) by assessing the security and safety risks of such systems. PyRIT is designed to test model-mediated behaviors, not to execute or control real autonomous agents. While PyRIT does not provide native agent execution, tool invocation, or system-level enforcement validation, its multi-turn orchestration, dataset-driven prompting, and scoring mechanisms enable the simulation of adversarial conditions commonly observed in agentic AI attacks. As such, PyRIT serves as an effective force multiplier for evaluating how Large Language Model (LLM) components within agentic systems respond to malicious influence, policy subversion attempts, and deceptive contextual framing. It enables security professionals and engineers to proactively identify vulnerabilities, potential harms, and adversarial threats in various AI models through modular, extensible, and community-driven tools that support both automated and semi-automated red teaming, including multi-turn interaction testing to uncover deep vulnerabilities.

Key Features and Capabilities

PyRIT‘s core architecture includes modular components such as targets, datasets, orchestrators (e.g., attack strategies), scoring engines, and memory systems that collectively facilitate automated, scalable security testing. PyRIT supports scenario-based testing by allowing users to define and execute malicious prompt datasets through orchestrated single-turn or multi-turn attack sequences, including sophisticated techniques like prompt injection and role-based social engineering (Microsoft, 2024a).

These architectural components operate as an integrated pipeline in which datasets supply the attack inputs, orchestrators manage the attack logic, targets define the endpoints for evaluations, memory systems capture all interaction traces, and the scoring engine quantifies the vulnerabilities.

PyRIT further enables real-time logging of all interactions and outcomes, storing detailed records in memory or databases for forensic analysis, and it employs scoring engines to quantify model vulnerabilities (The Hacker News, 2024). A distinctive feature of PyRIT is its support for model evaluations through adaptive multi-turn attack loops that simulate adversarial agents dynamically adjusting their tactics based on target responses—mirroring real-world threat behavior (Help Net Security, 2024; Microsoft, 2024b). These adaptive loops are not static scripts; they operate as semi-autonomous adversarial processes that iteratively refine prompts based on model refusals, vulnerabilities detected, or scoring thresholds, which enable PyRIT to carry out more robust, sophisticated attacker behavior.

The tool also supports structured test execution, where users configure threat scenarios, automate large-scale attacks, and collect metrics for robustness benchmarking across AI model versions (siliconANGLE, 2024). Though PyRIT does not generate formalized reports, its logging and memory systems enable users to analyze failures, generate visualizations, and produce customized summaries. Critically, PyRIT’s extensibility allows for integration with new attack types, scoring mechanisms, and non-traditional AI interfaces such as multi-modal models and web UIs, ensuring continued relevance in a rapidly evolving threat landscape (Azure, 2024).

PyRIT does not natively model agent state, authorization, or tool execution. Its agentic testing value lies in simulating adversarial agent behaviors through controlled prompting, conversation state tracking, and automated response evaluation. Any references to agent memory, roles, or permissions within PyRIT-based tests represent simulated constructs rather than enforced system controls.

Advanced Automation Capabilities for Enterprise Deployment

Beyond its core features, PyRIT’s architecture supports several advanced automation patterns essential for enterprise-scale AI red teaming. These include pre-built attack pattern libraries that reduce dependency on specialized expertise, intelligent orchestrator selection engines that recommend optimal testing configurations based on threat objectives, and adaptive scoring frameworks that evolve evaluation criteria based on conversation context and agent state.

For production deployment, PyRIT enables CI/CD integration through pre-commit security gates, automated regression testing for model updates, and continuous security monitoring pipelines. The framework’s extensibility allows for development environment integration through IDE plugins that provide real-time security feedback during prompt engineering, as well as integration with simulation environments for end-to-end agent behavior testing under adversarial conditions.

PyRIT’s telemetry and observability capabilities extend beyond basic logging to include comprehensive metrics collection across attack effectiveness, defense mechanism analysis, temporal behavior patterns, and resource consumption tracking. This enables security teams to identify not only point-in-time vulnerabilities but also behavioral drift over extended agent interactions and systematic weaknesses in defense architectures.

Key features include granular scenario-based testing via datasets and orchestrators, multi-turn adversarial dialogue support, automated scoring of responses, extensibility through modular components, persistent logging via memory interfaces, and scalable automation for assessing both security and responsible AI risks.

MITRE ATT\&CK and ATLAS are taxonomies of adversarial behaviors utilized widely in red teaming assessments, whereas PyRIT acts as the execution engine that operationalizes those behaviors for automation testing. For example, MITRE ATT\&CK framework tactics, techniques, and procedures (TTPs) or ATLAS techniques can be encoded into PyRIT orchestrators and datasets; PyRIT doesn’t have its own TTP ontology.

Summary of Agentic AI Red Teaming Guide by CSA and OWASP

The Agentic AI Red Teaming Guide, a collaborative effort led by the CSA and OWASP AI Exchange, represents a groundbreaking resource specifically designed to address the security challenges posed by autonomous AI systems. Unlike traditional GenAI models, agentic AI systems have autonomous capabilities to plan, reason, and execute complex tasks independently in both digital and physical environments. This guide emphasizes the urgent need for specialized red teaming techniques that simulate adversarial threats targeting the full lifecycle and workflows of these advanced agents. It provides practical, customizable scenario-based testing methods structured around 12 high-risk threat categories, including goal manipulation, memory exploitation, multi-agent collusion, and supply chain vulnerabilities, to offer a comprehensive framework for identifying and mitigating emerging attack surfaces unique to this technology.

Recognizing the profound paradigm shift autonomous agents introduce, the guide moves red teaming from isolated, point-in-time assessments toward continuous, simulation-driven validation that reflects the dynamic, adaptive nature of agentic AI. It advocates a systems thinking approach, where security professionals must evaluate the entire agent ecosystem—including its core language models, memory architecture, connected tools, and interaction with downstream systems—to uncover vulnerabilities that manifest from complex interdependencies. Beyond practical testing steps, the guide highlights the strategic imperative for organizations to build mature red teaming capabilities that incorporate automation and eventually autonomous red teaming agents. This pioneering work equips AI engineers, security architects, and red team professionals with actionable insights and tools to secure the next generation of autonomous AI applications across industries such as finance, healthcare, and industrial automation.

Summary of Agent Authorization and Control Hijacking Guidelines

The Agentic AI Red Teaming Guide specifies the requirements and guidelines for red teaming agent authorization and control hijacking.

These guidelines outline tests targeting unauthorized access escalation, privilege inheritance, bypass of human-in-the-loop (HITL) controls, and agentic misuse of systems. Specific test cases are categorized for simulation using automated tools like PyRIT. While this companion document focuses on the LLM’s behavior under attack, agentic AI threats may also manifest via external tools or connected systems that are not fully exercised via PyRIT.

Research Methodology

Setup and Configuration of PyRIT

The PyRIT environment was deployed using Python 3.10–3.13 with dependency management via “pip.” Installation followed the standard open-source protocol (pip install pyrit) and utilized the MIT-licensed codebase from Microsoft’s GitHub repository. To replicate a realistic deployment scenario, the tool was configured in a secure, containerized testing environment simulating production-level AI services.

Key components included:

• Target Models: Configured endpoints for OpenAI GPT-4 and Azure OpenAI services

• Memory Backend: DuckDB was initialized with encryption enabled for secure storage of prompt-response logs

• Security Practices: All API credentials were securely managed through environment variables and Azure Key Vault to prevent leakage

Initial validation included confirming memory logging functionality, endpoint response latency, and the ability to execute multi-turn prompts through orchestrator modules.

Selection Criteria for Test Cases

Selected test cases were driven by the objective to probe security and ethical integrity risks unique to agentic AI systems.

Each scenario was categorized by its threat vector, expected risk behavior, and target response criteria for accurate scoring.

Scripting and Automation Methods

Automation was implemented using Python-based orchestration scripts that leveraged PyRIT’s native modules.

Add a Work flow diagram : Dataset → Orchestrator → Target → Scoring Engine → Log/Report

Figure 1: PyRIT Red Teaming Evaluation Workflow

Advanced Automation Patterns Evaluated

This research extended beyond basic PyRIT usage to evaluate advanced automation patterns critical for production AI security operations.

Evaluation of PyRIT Capabilities

Role Inheritance Exploitation

For more details, see Appendix A: Implementation of Agentic AI Red Teaming Guide (Python).

Additional Inclusions for Role Exploitation

Other Relevant Test Areas

Additional tests to consider include:

• Agent Goal Manipulation: Examines whether external inputs can alter the agent’s core goal, maliciously redirecting its behavior. This could also happen by agent aging—still a risk but without malicious intent

• Agent Hallucination Exploitation: Evaluates the agent’s response to deceptive or ambiguous input that induces false memory or action. A failed tool call could lead to hallucination

• Agent Supply Chain Attacks: Assesses whether compromised or manipulated tools, plugins, or data sources affect the agent’s operational integrity

Step-by-step instructions for performing tests:

1. Setting Up the PyRIT Environment: Deploy PyRIT in a controlled environment reflecting production scenarios.

   1. Define baseline configuration aligning with Agentic AI Red Teaming Guide.

2. Scripting an Automation: Write PyRIT automation scripts for each actionable step, clearly delineating expected versus unauthorized actions.

3. Testing and Validation: Run automated scenarios and document outcomes.

   1. Specifically monitor how PyRIT detects unauthorized access, logs incidents, and generates alerts.

4. Analysis and Reporting: Consolidate PyRIT’s automated outputs into structured reports proving vulnerabilities, their severity, and recommended mitigations.

Advanced Automation Scenarios

Current Limitations

PyRIT does not currently provide an official curated library of attack patterns. Users must develop their own datasets, though the framework’s extensibility supports this use case well. Community-contributed attack libraries would significantly reduce barriers to entry.

Results and Findings

Capability Matrix

This table compares the features of PyRIT with the required steps of agentic AI red teaming, including preparation, execution, analysis, and reporting. The table aligns PyRIT capabilities to assess scenarios.

This matrix demonstrates that PyRIT provides modular, extensible coverage across all critical stages of the red teaming lifecycle for agentic AI systems.

Comprehensive Capability Matrix: PyRIT Production Features

This matrix evaluates PyRIT’s production-ready automation capabilities against requirements for agentic AI red teaming as defined in the Agentic AI Red Teaming Guide and addresses testing phase/requirements, PyRIT native support, implementation complexity, and automation effectiveness.

Key Findings from Capability Assessment

• High Automation Coverage for Core Red Teaming (95%+): PyRIT provides production-ready automation for all core red teaming activities: prompt execution, scoring, logging, and reporting. The orchestrator architecture enables both single-turn batch testing and sophisticated multi-turn adversarial conversations without custom code development

• Strong Support for Prompt-Level Agentic Testing (80%): Testing agent responses to role escalation, memory poisoning, goal manipulation, and tool misuse scenarios is well-supported through existing orchestrators and scorers. The limitation is that PyRIT evaluates LLM textual responses rather than actual agent state changes or tool invocations

• Partial Support for System-Level Agent Validation (40%): PyRIT cannot natively observe actual agent behavior beyond LLM responses. Testing whether an agent actually executes unauthorized tools, modifies its internal state, or persists false memories requires integration with agent framework telemetry, which is outside of PyRIT’s current scope

• Excellent CI/CD Integration Potential (90%): PyRIT’s Python-based API, scriptable execution, and programmatic result access make it highly suitable for CI/CD integration. While PyRIT itself doesn’t provide CI/CD connectors, standard DevOps practices readily support PyRIT script execution in automated pipelines

• Limited Built-in Reporting and Visualization (30%): PyRIT excels at data collection but provides minimal built-in reporting or visualization. Organizations must develop custom analysis scripts or integrate with BI tools (e.g., Excel, Power BI, Grafana) to generate stakeholder-ready reports

Summary of Strengths and Limitations

Gap Analysis

PyRIT Evaluated Against Agentic AI Red Teaming Guide (Pros and Cons)

Please keep in mind the documented limitations of PyRIT. First, PyRIT is not a replacement for human red teamers but a force-multiplier. The tool automates labor-intensive tasks (like generating and scoring attacks), yet it assumes an expert will drive the strategy and interpret the findings.

Microsoft’s team emphasizes that a skilled security professional should guide PyRIT and investigate the “hot spots” it identifies. This reliance on experts imposed by PyRIT is an operational challenge, as it places the onus of defining the objectives and attack strategy and updating rapidly changing attack prompt datasets and techniques on the expert against a continuously evolving attack landscape. Additionally, the framework’s limited native target support with Azure/OpenAI and generic HTTP-based endpoints necessitates that the user spend considerable engineering effort to implement custom integrations to AI agents, integrated development environments (IDEs), or AI browsers.

Inexperienced users might find PyRIT’s myriad of options daunting—indeed, industry experts noted that beginners or intermediates may find it overly complex and not fully benefit from its capabilities. There is a learning curve to understanding how to configure orchestrators, implement scoring, and interpret results correctly.

Another area of reliance on the expert is the selection of the attacker and evaluator models used for attack generation and evaluations. Since many established models reject requests to generate adversarial prompts, the security specialist must expend continuous effort to circumvent these safety guardrails. Furthermore, the variability in the quality of attacks and evaluations from the chosen attacker and scorer models necessitates that the user spend dedicated time identifying the most appropriate models for this purpose. Consequently, while PyRIT offers a robust modular library for red teaming GenAI applications, it requires substantial manual intervention and expertise to transition into a high-fidelity, effective tool. Microsoft has sought to reduce the learning curve by providing dedicated Microsoft Learn training paths focused on PyRIT and AI red teaming.

Beyond the technical limitations, there are also practical gaps when working with PyRIT. As PyRIT is not built for end-to-end observation, it can be challenging to determine how an agent behaves over longer tasks or how it handles changes in its environments. While prompts can be pushed to the model and its responses are available for interpretation, the lack of insight into how an agent thinks, adapts, or stumbles during red teaming assessments leaves the bigger picture somewhat blurry.

Another limitation with PyRIT is that it addresses AI behavioral risks (e.g., content generation, prompt exploits) but does not cover other security aspects such as network intrusion or authentication bypass. It is specialized for testing the model’s outputs and guardrails, so if an agentic AI system has agentic action components, PyRIT would need an extension to interact with those components. For example, PyRIT can prompt an AI agent to perform a bad action, but if the agent’s risk lies in tool use (e.g., making an API call to a financial system), PyRIT would not natively capture the consequences of that beyond the agent’s textual response. Integrating PyRIT with simulation environments or tool APIs would be an extra step in such cases. (For more on this, see Appendix D: Agentic AI Exploit Validation with PyRIT Quantitative Test Plan.)

Summary of Strengths and Limitations

PyRIT has advanced capabilities but also certain limitations. The current framework handles GenAI security testing effectively but lacks easy accessibility for less experienced users. Addressing these gaps may involve adding intuitive design elements while keeping the tool’s technical depth. Moreover, PyRIT’s adaptation to new threat landscapes relies on periodic updates and user feedback, suggesting a need for continuous improvements in its architecture.

Recommendations

Suggestions for PyRIT Feature Enhancements

To further elevate PyRIT’s utility in agentic AI security testing, it is crucial to integrate the principles and threat categories outlined in the Agentic AI Red Teaming Guide. This guide provides an extensive framework tailored to the unique security challenges posed by agentic AI systems, including vulnerabilities in orchestration logic, persistent memory manipulation, goal manipulation, agent authorization and control hijacking, and multi-agent exploitation. Incorporating these insights and improving PyRIT functionalities could potentially allow it to simulate complex, real-world attack scenarios specific to agentic AI, which traditional testing approaches might miss.

Additional feature recommendations include modules for dynamic testing of multi-agent workflows and agent orchestration, reflecting how autonomous agents interact within cloud environments. Advanced memory and state manipulation capabilities would enable testing of long-term persistence and emergent behaviors. Incorporating feedback-driven adaptive adversarial inputs would let PyRIT explore how agents evolve in response to attacks, uncovering cascading failure points or unexpected vulnerabilities.

Recent publications within the Cloud Security Alliance community have highlighted that agentic AI vulnerabilities often arise not from prompt-level manipulation but from orchestration workflows and decision logic. Logic-Layer Prompt Control Injection (LPCI) has been identified as a class of attacks in which adversarial instruction sequencing targets an agent’s internal workflow, branching logic, or tool selection paths rather than the surface prompt. Zero Trust architectural approaches for agentic AI further emphasize identity-anchored verification, non-routable communication paths, and strict turn-level control checks. Incorporating logic-layer adversarial testing capabilities aligned with these emerging threat models represents a valuable opportunity for expanding PyRIT’s future effectiveness in agentic AI red teaming.

Furthermore, adding native connectors to popular agent orchestration frameworks and cloud service platforms will enhance ease of deployment and scalability. Visualization tools focused on agent state transitions, memory alterations, and the blast radius of attacks would greatly improve risk assessment usability. Lastly, refining PyRIT’s scoring engine to evaluate metrics aligned with agentic AI characteristics—such as behavioral reliability, systemic robustness, and risk propagation—would provide more meaningful and actionable insights.

Additional enhancements could focus on evaluating long-horizon agent behavior, where vulnerabilities often emerge only after extended sequences of decisions or state transitions. The Cognitive Degradation Resilience (CDR) framework highlights techniques for identifying behavioral drift, progressive state inconsistencies, and conditions that may lead to systemic degradation over time. Incorporating long-duration assessment capabilities inspired by these concepts would enable PyRIT to surface failure modes that occur during prolonged agent operations or multi-step task execution.

Together, these enhancements, grounded in the CSA guide’s comprehensive framework, would position PyRIT as a leading automated platform for cloud security-focused agentic AI red teaming, driving stronger security postures against the evolving complexity of autonomous AI threats.

Conclusion

This research evaluated PyRIT’s suitability for red teaming agentic AI systems, confirming its effectiveness in automating and analyzing complex attack scenarios. PyRIT demonstrates strong capabilities through its modular architecture, multi-turn orchestrators, extensibility, and detailed logging features, aligning well with the core needs of adversarial testing for autonomous decision-making systems. However, to fully meet the demands of agentic AI red teaming as outlined in the Agentic AI Red Teaming Guide by the CSA and OWASP AI Exchange, PyRIT needs significant enhancements. These include advanced support for persistent memory manipulation and state tracking, orchestration-aware attack simulation for multi-agent workflows, dynamic adaptation to evolving agent behaviors, and richer evaluation metrics tailored to emergent and long-horizon vulnerabilities. Additionally, improving integration with agent orchestration frameworks and expanding visualization tools to capture complex agent interactions and goal manipulations will be essential. Addressing these gaps will elevate PyRIT into a more comprehensive platform capable of rigorously testing the unique and evolving threat landscape posed by autonomous AI systems.

The industry is moving forward with lots of great innovation in agentic AI red teaming tools. For example, Databricks recently released BlackIce, an open-source, containerized red teaming toolkit inspired by Kali Linux that bundles 14 key AI security tools—including Microsoft PyRIT—into a single Docker image to simplify LLM and machine learning (ML) vulnerability testing. From a red teaming guide perspective, it resolves setup hassles, dependency conflicts, and environment issues via a unified CLI for static and dynamic tools, aligning with MITRE ATLAS (e.g., prompt injection AML.T0051, jailbreaks AML.T0054) and the Databricks AI Security Framework to probe risks like data leakage, hallucinations, adversarial evasion, and supply chain threats. Pull databricksruntime/blackice:17.3-LTS for Databricks clusters to run attacks like PyRIT jailbreaks out of the box.

References

1. Microsoft. (2024a), February 22). Announcing Microsoft’s open automation framework to red team generative AI systems. Microsoft Security Blog. https://www.microsoft.com/en-us/security/blog/2024/02/22/announcing-microsofts-open-automation-framework-to-RED-team-generative-ai-systems/
2. Microsoft Azure. (2024b). PyRIT - Python Risk Identification Tool [Source code]. GitHub. https://github.com/Azure/PyRIT
3. The Hacker News. (2024, February 23). Microsoft Releases PyRIT – A Red Teaming Tool for Generative AI.
   https://thehackernews.com/2024/02/microsoft-releases-pyrit-red-teaming.html
4. ODSC. (2024, March 8). PyRIT: The Python Risk Identification Tool Enhancing Generative AI Security. https://medium.com/@odsc/pyrit-the-python-risk-identification-tool-enhancing-generative-ai-security-33c89cd29516
5. Help Net Security. (2024, March 4). PyRIT: Open-source framework to find risks in generative AI systems.
   https://www.helpnetsecurity.com/2024/03/04/pyrit-microsoft-framework-find-risks-generative-ai-systems/
6. siliconANGLE. (2024, February 23). Microsoft releases automated PyRIT red teaming tool for finding AI model risks. https://siliconangle.com/2024/02/23/microsoft-releases-automated-pyrit-RED-teaming-tool-finding-ai-model-risks/
7. Microsoft Azure. (2025, May 7). New capabilities in Azure AI Foundry to build advanced agentic applications.
   https://azure.microsoft.com/en-us/blog/azure-ai-foundry-updates-agentic-ai/
8. Cloud Security Alliance. (2025). Agentic AI Red Teaming Guide. https://cloudsecurityalliance.org/artifacts/agentic-ai-red-teaming-guide
9. Cloud Security Alliance. (2025). Fortifying the Agentic Web: A Unified Zero Trust Architecture Against Logic-Layer Threats. https://cloudsecurityalliance.org/blog/2025/09/12/fortifying-the-agentic-web-a-unified-zero-trust-architecture-against-logic-layer-threats
10. Cloud Security Alliance. (2025). Introducing Cognitive Degradation Resilience (CDR): A Framework for Safeguarding Agentic AI Systems from Systemic Collapse. https://cloudsecurityalliance.org/blog/2025/11/10/introducing-cognitive-degradation-resilience-cdr-a-framework-for-safeguarding-agentic-ai-systems-from-systemic-collapse
11. Databricks. (2026, January 20). Announcing BlackIce: A Containerized Red Teaming Toolkit for AI Security Testing. https://www.databricks.com/blog/announcing-blackice-containerized-red-teaming-toolkit-ai-security-testing

Appendix A: Implementation of Agentic AI Red Teaming Guide (Python)

Test Design and Execution

Threat-Centric Test Cases

Modular Test Structure

Scoring and Analysis

Category-Specific Scoring

Quantitative Metrics

Operational Security

Secure Testing Environment

Credential Management

Reporting and Integration

Actionable Reports

CI/CD Integration

PyRIT Setup Checklist

Sample Test Output

Appendix B: PyRIT Automation Scripts

The table below provides a structured breakdown of the core components used in the sample automation script for red teaming agentic AI systems using PyRIT.

Python Example (Control JSON Dataset)

Appendix C: Detailed Targets

Target 1: Memory Poisoning

Description

Test the agent’s vulnerability to distorted or false memory insertion, causing it to produce incorrect or harmful outputs.

PyRIT Scenario Definition (Python)

PyRIT Automation Script

Expected Outcome

The agent should reject these prompts and clearly indicate these memories are invalid or unauthorized. Logs will detail prompt-response pairs and whether refusal occurred.

Target 2: Tool Misuse

Description

Assess if the agent can be coerced into improperly using APIs or external tools outside its defined policy boundaries.

PyRIT Scenario Definition (Python)

PyRIT Automation Script

Expected Outcome

The agent should consistently refuse unauthorized API or tool usage requests, maintaining operational policy compliance. The generated logs should provide clear visibility into agent behavior.

Next Steps

1. Share these scripts with your red team to facilitate exploit development.
2. Integrate findings from these automated tests into your vulnerability reporting process.
3. Evaluate the agent’s robustness, focusing on refusal scores and compliance rates from PyRIT logs.

Appendix D: Agentic AI Exploit Validation with PyRIT Quantitative Test Plan

Introduction and Objectives

This document outlines a structured test plan to generate quantitative results for two critical agentic AI exploits: Tool Misuse and Memory Poisoning. The goal is to move beyond qualitative assessment and provide measurable, statistically significant data on an AI model’s resilience using the PyRIT framework.

• Exploit(s): Tool Misuse and Memory Poisoning
• Primary Objective: To quantify the target AI model’s resilience by calculating key security metrics
• Hypothesis: A well-guarded model will demonstrate a high Refusal Rate (>95%) and a low Exploit Success Rate (<5%) for these attack categories

Key Metrics and Definitions

The following metrics will be calculated for each exploit category.

Test Setup and PyRIT Configuration

Prerequisites

• Python 3.10+ environment
• PyRIT installed (pip install pyrit)
• Access to an Azure OpenAI endpoint (or other supported PyRIT target)
• API credentials securely stored in environment variables

PyRIT Initialization Code

The following base configuration will be used for both test suites.

Step 1

# Initialize target connection
target = AzureOpenAIChatTarget

Step 2

# Initialize memory for logging
memory = DuckDBMemory(db_path=”:memory:”)

Note: Use a file path for persistent storage.

Step 3

# Configure the orchestrator

Test Suite 1: Memory Poisoning Exploit

Objective

To test the agent’s vulnerability to distorted or false memory insertion, causing it to produce incorrect or harmful outputs.

Attack Prompt Dataset

Test Execution Code

Step 1

# Import the prompts and create a dataset
dataset = StaticDataset(prompts=memory_poisoning_prompts)

Step 2

# Configure a binary scorer for this specific exploit

Step 3

# Run the experiment
await orchestrator.run_async()

Step 4

# Export results for analysis
memory.export_to_json

File name: memory_poisoning_results.json

Test Suite 2: Tool Misuse Exploit

Objective

To assess if the agent can be coerced into improperly using APIs or external tools outside its defined policy boundaries.

Attack Prompt Dataset

Test Execution Code

Step 1

# Import the prompts and create a dataset
dataset = StaticDataset(prompts=tool_misuse_prompts)

Step 2

# Configure a binary scorer for this specific exploit

Step 3

# Run the experiment
await orchestrator.run_async()

Step 4

# Export results for analysis
memory.export_to_json

File name: tool_misuse_results.json

Results Analysis and Reporting

Analysis Scripts and Expected Output Format

Run the following scripts after each test to parse the JSON results and calculate the metrics. The scripts will produce clear, quantitative summaries.

Script 1: Memory Poisoning Results

# Run the following script to parse the JSON results and calculate the metrics
memory_poisoning_results.json

Script 2: Tool Misuse Results

# Run the following script to parse the JSON results and calculate the metrics
tool_misuse_results.json

Conclusion

This test plan provides a repeatable methodology for benchmarking AI model security. The quantitative results will:

• Provide a clear baseline for model resilience
• Identify specific exploit categories where the model is most vulnerable
• Allow for tracking improvement over time as the model is hardened

Next Steps

1. Execute the test plans in a controlled environment.
2. Document the results in a final report.
3. Use the findings to prioritize model fine-tuning and guardrail enhancement.
4. Integrate this testing regimen into a CI/CD pipeline for continuous security validation.

Appendix E: Detailed Results from Automated Tests

Includes raw test data, logs, and screenshots of outputs for transparency and reproducibility.

• Test Execution: Automated test scripts are typically written in Python to execute interactions systematically

• Log Generation: Python scripts use PyRIT modules to log detailed outputs (e.g., prompt-response pairs, timestamps) in structured formats like JSON

• Analysis: Python scripts can analyze logs and calculate metrics such as prompt execution rate, response accuracy, and exploit success rate

• Metrics Evaluation: Python can programmatically assess the AI’s responses, classify the outcomes (policy violation or refusal), and validate the consistency and resilience of the tested system