Red Teaming and Stress-Testing AI
Before you ship an AI model, you want to know how it fails when someone actually tries to break it. “Red teaming” is the practice of simulating real attacks – prompt injections, evasive inputs, adversarial examples, and misuse – so you can find the cracks before the internet does.
This page walks through what red teaming is, what kinds of attacks to test, and how it fits into deployment.
What is Red Teaming in AI?
In cybersecurity, a “red team” plays the attacker to test defenses. In AI, it’s the same idea:
you deliberately probe a model (and the system around it) with adversarial inputs to uncover security, safety, privacy, and reliability failures that standard QA often misses.
The point isn’t to “gotcha” the model – it’s to map real-world failure modes. Models are probabilistic and context-sensitive, so the weird edge cases are often the dangerous ones. Red teaming makes those edge cases show up before deployment.
Why Standard QA Isn’t Enough
Traditional testing is great at confirming expected behavior. But attackers don’t behave “as expected.”
They look for instruction hierarchy bugs, jailbreak phrasing, weird Unicode tricks, indirect prompt injections through tools/RAG, and anything that causes data leakage, policy bypass, or unsafe actions.
Red teaming complements normal evaluation by intentionally stress-testing the system under hostile conditions—like a fire drill for your model.
01
Adversarial Inputs (Vision + Classifiers)
Tiny input changes—sometimes invisible to humans—can cause big model mistakes. In vision, this can look like subtle pixel noise or small physical stickers that flip an image classifier’s decision.
These tests matter anywhere misclassification creates real risk (autonomy, security screening, medical imaging, etc.).
02
Prompt Injection + Jailbreaks (LLMs)
For LLMs, attackers try to override rules with instructions like “ignore previous directions,” role-play, multi-step coercion, or indirect injection via documents/web pages that the model reads. The goal is usually one of: leaking hidden instructions, revealing private data, producing disallowed content, or manipulating downstream tools.
03
Model Evasion + Filter Bypass
Even when you add safety layers, users can try to route around them: rephrasing, using coded language, spreading a request across multiple turns, or exploiting emotional framing and ambiguity.
Red teams look for the “paths of least resistance” that your guardrails didn’t anticipate.
04
Data Poisoning + Training-time Attacks
If attackers can influence training/fine-tuning data, they can embed backdoors, skew behavior, or increase memorization of sensitive content. These tests are especially relevant for pipelines that ingest user data, public web data, or third-party datasets.
01
Scope the system (not just the model)
Define what you’re actually shipping: model + prompt + tools + browsing/RAG + memory + policies + UI. Decide what “bad” looks like (data leakage, policy bypass, harmful outputs, unsafe actions), and what success metrics you’ll track.
02
Run red team “sprints” (humans + automation)
Combine creative human testing (the “how would I break this?” part) with automated attack generation at scale. Good red teams include internal security folks, engineers, and often external specialists who bring fresh attacker instincts.
03
Patch, retest, and turn failures into regression tests
Every high-quality red team output becomes: (1) a fix (prompt hardening, safety tuning, tool permissions, sandboxing), and (2) a repeatable test case you can run forever. This is how you stop “whack-a-mole” and start compounding safety over time.
Real-World Examples
Red teaming isn’t theoretical. We’ve seen chat systems leak their hidden instructions, vision models misread road signs, and agentic systems get manipulated into unsafe actions via indirect prompt injection. These stories aren’t here for drama – they’re here because they show where systems break in practice.
Bing Chat Prompt Leak
A real-world example of how a clever prompt can pressure a system into revealing hidden instructions or behaviors. If it can be extracted, assume it will be extracted.
Stop Sign Adversarial Attacks
Researchers have shown small visual modifications can cause consistent misclassification. In safety-critical settings, “mostly accurate” isn’t good enough.
Tesla Autopilot Sticker Trick
A reminder that robustness has to survive the messy physical world: lighting, angles, noise,
and intentional manipulation.
Indirect Prompt Injection via Tools/RAG
When models read web pages, PDFs, emails, or docs, attackers can hide instructions inside that content – tricking the model into doing something unsafe.
Filter Evasion in the Wild
Bypasses often look boring: rephrasing, multi-turn setup, or emotional framing that changes the model’s judgment. Red teaming finds these “soft spots.”
Training Data Risks
Data supply chains are attack surfaces: poisoning, backdoors, and accidental memorization.
Good governance is part of good engineering.
Pre-Deployment Stress Testing
Catch the worst failures before launch: scoped tests, severity scoring, and fixes before users ever touch it.
System-Level Red Teaming
The model might be “fine” but the product isn’t. Tool permissions, logging, memory, and UX can create the real vulnerability.
Continuous Red Teaming
New model versions + new attacks = continuous testing. Turn findings into a living test suite and keep shipping safely.
If you don’t stress-test your AI like an attacker would, you’re basically outsourcing that job to strangers.
Red teaming turns “surprise failures” into “known issues” you can actually fix.
Want a red team pass?
Want to red team your AI before launch?
Prompt injection testing, tool/RAG abuse scenarios, data leakage probes, and a practical list of fixes + regression tests.
Want to stress-test an AI system you’re about to ship?
If you’ve got a model going into production (or already in production), let’s pressure-test it.
Call me: 404.590.2103