AI Algorithmic Red Teaming Tools, Resources, and Mindmap
This week I am presenting two sessions at Cisco Live EMEA:
- Red vs. Machine: A Practical Exploration of AI Algorithmic Red Teaming
- Securing Retrieval Augmented Generation (RAG) Implementations and the LLM Stack
The following is the agenda for the “Red vs. Machine: A Practical Exploration of AI Algorithmic Red Teaming” session:
I go over the methodologies, frameworks, and tools necessary for testing and securing AI systems. This session was designed for security professionals, AI researchers, red teamers, and anyone interested in strengthening AI security through offensive security techniques.
Understanding attack vectors is very important, but applying methodologies systematically makes AI red teaming effective.
Note: Some of the best guides to understand the attack vectors are:
The OWASP AI Security and Red Teaming Guide introduces best practices for evaluating AI security. I go over a practical walkthrough of OWASP’s AI security methodologies and discuss key recommendations for adversarial testing.
Online Mind Map of Open Source AI Security Tools
The OWASP AI Security Testing section of the OWASP AI Exchange also includes a series of tools to stress test AI models. They have static mindmaps of these tools. Although, I created an online mind map of open-source tools specifically designed for AI security assessments, which includes a couple of tools that are not covered in OWASP’s site.
You can access the tool at: https://tools.aisecurityresearch.org
The mind map has the links to each of the tools.
Testing Methodologies: From Prompt Injection to RAG Security
To truly secure generative AI, red teamers use a variety of testing methodologies. These are five key areas:
Prompt Injection Attacks
Prompt injection involves designing input sequences that trick an AI into ignoring its safety protocols. For example, an attacker might append an instruction that says, “ignore all previous rules,” coaxing the model into producing unauthorized content.
How It’s Tested:
- Manual crafting of known jailbreak prompts (like the “DAN” prompt).
- Automated generation using fuzzers to test variations.
- Multi-turn adversarial interactions where the attacker gradually persuades the model to deviate from its rules.
Impact:
Successful injection attacks can force the model to reveal sensitive internal instructions, produce harmful content, or even extract confidential data.
Model Extraction Attempts
Model extraction tries to reconstruct the behavior — or even the training data — of a proprietary model. By repeatedly querying the AI, an attacker might approximate its decision boundaries or retrieve memorized data.
How It’s Tested:
- Repeated querying using both direct and indirect prompts.
- Incorporating “canary tokens” in training data and then attempting to retrieve them.
- Simulating oracle distillation, where a red team builds a surrogate model from the target’s responses.
Impact:
A successful extraction compromises intellectual property and may reveal sensitive training data, leading to significant privacy and competitive risks.
Data Leakage Vulnerabilities
Data leakage occurs when an AI inadvertently discloses sensitive information — either from its training data or from hidden system prompts.
How It’s Tested:
- Attempting to reveal the hidden system prompt through creative rephrasings.
- Injecting content designed to elicit confidential data.
- Observing outputs for unexpected sequences that resemble private information.
Impact:
Even a small leakage can have severe ramifications, from violating user privacy to exposing trade secrets.
Alignment and Safety Testing
This involves verifying that an AI model adheres to its ethical and safety guidelines even when confronted with adversarial inputs.
How It’s Tested:
- Directly asking the model for disallowed advice (e.g., instructions for illegal activities) to see if it complies.
- Engaging in adversarial dialogue where the model’s ethical boundaries are probed over multiple turns.
- Utilizing datasets that stress-test for biased, hateful, or otherwise harmful outputs.
Impact:
Proper alignment testing ensures that AI outputs remain within acceptable ethical boundaries, thereby protecting users and upholding legal and reputational standards.
RAG (Retrieval-Augmented Generation) Security Testing
RAG architectures combine LLMs with external data retrieval systems (like data from vector databases, see the paper on how to secure vector databases and the guidance from OWASP). While this enhances the AI’s factual accuracy and relevance, it also introduces vulnerabilities — such as data poisoning or inadvertent exposure of sensitive documents.
How It’s Tested:
- Injecting malicious data into the retrieval database and observing if the model propagates it.
- Verifying that access controls in the retrieval system prevent unauthorized information leakage.
- Testing for embedding inversion attacks, where the vector representation is exploited to reconstruct sensitive content.
Impact:
Vulnerabilities in RAG systems can lead to compromised external data sources influencing the AI’s output, thus eroding trust and security across integrated applications.
A Few New Techniques
AI security is evolving rapidly, and you must adopt proactive measures to secure you AI applications and systems. You can use these open source tools to evaluate your applications. However, you have to also stay ahead of new techniques, research, and methodologies. For example, my colleagues Adam Swanda and Emile Antone went over new AI model attack research in a recent blog post. The most notable AI attack research was:
SATA: Jailbreak via Simple Assistive Task Linkage. Researchers demonstrated an attack success rates of 85% using masked language model (MLM) and 76% using element lookup by position (ELP) on the AdvBench dataset.
Another one is the Single-Turn Crescendo Attack (STCA), which was covered in a paper titled “Well, that escalated quickly: The Single-Turn Crescendo Attack (STCA)”. It simulates an extended dialogue within a single interaction to an AI model. It works well against latest models like o3-mini:
The last one they covered is the Jailbreak through Neural Carrier Articles, which embeds prohibited queries into benign carrier articles in order to bypass AI model guardrails.
Hope this is useful for you. As you probably know, I have thousands of other cybersecurity and AI security resources in my GitHub repository at: https://hackerrepo.org and at https://hackertraining.org.
