Find your AI's weaknesses before attackers do
LangProtect AI Red Teaming helps identify prompt injection, jailbreaks, sensitive data exposure, unsafe outputs, and other AI-specific risks before deployment.
Why AI application need Red Teaming
Traditional testing shows whether your AI works. Red teaming reveals what happens when someone tries to break it.
Unpredictable AI behaviour
Small changes in language or context can trigger unexpected behavior.
New attack surfaces
Prompts, models, data, and connected tools create new paths for attack.
Hidden security gaps
Normal testing can miss vulnerabilities that surface under adversarial inputs.
Constantly evolving risk
Model, prompt, and application changes can introduce new weaknesses.
Test your AI against the real world threats
See how your AI responds to adversarial attacks designed to expose weaknesses in its security, safety, and behavior.
Prompt leakage
Policy violations
Malicious URLs
Prompt injection
Secrets & API keys
Jailbreaks
Toxic and harmful content
Sensitive data exposure
Prompt leakageToxic and harmful contentMalicious URLsSecrets & API keysPolicy violationsSensitive data exposurePrompt injectionJailbreaks
How LangProtect AI Red Teaming works
Put your AI through a structured adversarial testing process to uncover weaknesses, understand risk, and strengthen defenses.
See what your AI is really exposed to
Turn adversarial testing into a clear view of where your AI is vulnerable and what needs attention.
Security weaknesses
Discover where attackers can bypass safeguards, manipulate AI behavior, or exploit weaknesses in your application.
Data exposure risks
Identify scenarios where sensitive data, credentials, system information, or proprietary content could be exposed.
Unsafe AI behaviour
Surface harmful, biased, toxic, or unintended responses that could create safety and reputational risks.
Governance Gaps
Reveal where AI behavior falls outside organizational policies, privacy requirements, or acceptable-use standards.
Discover. Strengthen. Protect.
Discover / Test / Expose
Challenge your AI with adversarial scenarios to uncover vulnerabilities, unsafe behavior, and weaknesses before they can be exploited.
Analyze / Fix / Validate
Turn red teaming findings into actionable improvements across prompts, guardrails, application logic, and security policies.
Detect / Block / Protect
Extend those defenses into production with real-time protection that detects and blocks AI-specific threats as interactions happen.
Secure AI from pre-production to runtime
Test before deployment, validate changes as your AI evolves, and extend protection into production with LangProtect Armor.
Build
Build AI applications, copilots, RAG systems, and agents around your business requirements.
- Models & LLMs
- System Prompts
- RAG Pipelines
- AI Agents
Pre-Production
Stress-test your AI against adversarial attacks and uncover security, safety, and governance weaknesses before launch.
- Adversarial Testing
- Vulnerability Discovery
- Safety Testing
- Policy Validation
Remediate
Address identified weaknesses and strengthen the controls surrounding your AI before it reaches production.
- Fix Vulnerabilities
- Strengthen Guardrails
- Update Policies
- Retest Defenses
Runtime
Continuously inspect AI interactions and enforce security policies against threats that emerge in production.
- Real-Time Detection
- Threat Blocking
- Data Protection
- Policy Enforcement
When should you Red Team Your AI?
Test at critical moments to uncover new weaknesses before they become production risks.
Before production
Stress-test new AI applications before deployment to uncover security, safety, and governance weaknesses early
After major changes
Retest after changing models, system prompts, data sources, guardrails, or application logic.
Before security reviews
Evaluate AI behavior against adversarial scenarios and provide security teams with evidence of identified risks.
After remediation
Retest identified weaknesses to confirm that remediation efforts and updated controls work as intended.
For ongoing AI assurance
Periodically reassess AI systems as applications evolve and new attack techniques emerge.
