AI Red Teaming & Safety Services | Adversarial Testing | JudgeMyAI
SERVICE // ADVERSARIAL DEFENSE

AI Red Teaming &
Safety Evaluation

Don't wait for the internet to break your model. Our human red teamers—former cybersecurity experts and adversarial ML researchers—stress-test your LLMs against prompt injections, jailbreaks, and novel attack vectors before deployment.

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RED_TEAM_OPS.log
CORE COMPETENCIES

AI Security Fundamentals

Semantic definitions of our adversarial testing and AI safety methodologies.

  • AI Red Teaming The practice of simulating cyberattacks and adversarial inputs on AI models to identify vulnerabilities, biases, and safety failures. Human experts actively attempt to bypass guardrails to discover "zero-day" flaws before deployment.
  • Prompt Injection A malicious technique where attackers embed hidden instructions within data processed by the LLM (e.g., web pages or documents), attempting to override the model's system prompt and execute unauthorized commands.
  • Jailbreaking The use of specific phrasing, psychological manipulation, or character roleplay to convince the AI to bypass its safety filters and generate restricted, harmful, or unethical content.
  • Adversarial Suffixes The appending of specific, mathematically generated sequences of characters to prompts to break model alignment. Our experts manually craft and test these to ensure your reward model holds firm.
ATTACK VECTORS

The Threat Matrix

We simulate the full spectrum of adversarial attacks to harden your model's guardrails.

CRITICAL

Prompt Injection

Testing for hidden command execution via external data sources, cross-prompt contamination, and system prompt overrides.

HIGH

Jailbreaks & DAN

Attempting to bypass safety filters using "Do Anything Now" prompts, roleplay scenarios, and hypothetical framing.

HIGH

Data Exfiltration

Tricking the model into revealing its system prompt, training data (PII extraction), or internal API keys.

MEDIUM

Bias & Toxicity

Probing the model for demographic biases, hate speech generation, and culturally insensitive or harmful outputs.

THE PROTOCOL

Continuous Vulnerability Management

Red teaming isn't a one-time check. It's a continuous cycle. As your model learns and evolves, new attack surfaces emerge. Our experts integrate directly with your ML team to provide ongoing adversarial feedback.

We map the attack surface, execute simulated breaches, and deliver actionable preference data to retrain your reward models against the discovered vulnerabilities.

PHASE 01

Surface Mapping

Identifying model capabilities, API endpoints, and data access points.

PHASE 02

Adversarial Gen

Crafting novel, zero-day attack prompts tailored to your model.

PHASE 03

Breach Execution

Actively exploiting vulnerabilities to bypass safety guardrails.

PHASE 04

Patching (RLHF)

Delivering preference data to retrain and harden the reward model.

340+
Vulnerabilities Found
99.9%
Guardrail Bypass Blocked
0
Post-Deploy Critical Exploits
INTEL

Red Teaming FAQs

What is AI red teaming?
AI red teaming is the practice of simulating adversarial attacks on AI models to uncover safety flaws, vulnerabilities, and alignment issues. It involves human experts actively trying to bypass guardrails using prompt injection, jailbreaks, and edge-case scenarios before the model is deployed.
What is the difference between prompt injection and jailbreaking?
Prompt injection involves embedding malicious instructions within data the AI processes, attempting to override its system prompt. Jailbreaking is the use of specific phrasing or psychological manipulation to convince the AI to bypass its safety filters and generate restricted content.
Why do we need human red teamers instead of automated safety scanners?
Automated scanners only catch known vulnerabilities. Human red teamers, especially former cybersecurity experts, use creative critical thinking to discover novel 'zero-day' attack vectors that automated scripts and reward models completely miss.
INITIALIZE DEFENSE

Ready to secure
your model?

Deploy elite red teamers. Patch vulnerabilities. Ship safe models.

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