Black
AI Hardening

Automate AI Red Teaming

Continuously test AI models and applications against realistic adversarial attacks. Identify weaknesses early and validate fixes through repeatable assessments.

Use Case

AI models and applications need security testing that reflects how adversaries actually manipulate AI behavior. PointGuard AI automates red teaming and scanning so teams can identify weaknesses earlier and validate that AI systems are ready for deployment.

Challenges

The AI environment creates several recurring security and governance challenges:

  • Traditional testing misses AI-specific behavioral failures
  • Manual red teams cannot cover every release consistently
  • Models and applications regress as prompts and controls change
  • Teams need repeatable evidence before deployment

Solution

PointGuard AI provides a comprehensive solution for continuously probing models and applications with adversarial tests:

1. Scan AI assets. Use AI Red Teaming to assess models and connected applications for vulnerabilities, embedded malware, weaknesses, and other integrity risks.

2. Simulate adversarial attacks. Run automated tests for prompt injection, jailbreaks, unsafe behavior, bias, toxicity, and other AI-specific threat conditions.

3. Measure failures. Identify responses that violate security, safety, or trust expectations and prioritize the weaknesses that require remediation.

4. Retest after changes. Repeat adversarial testing as models, prompts, applications, and controls evolve to verify that fixes remain effective.

Continuous red teaming turns AI security testing into a repeatable engineering control rather than a one-time assessment.

Risks Addressed

Applicable framework risks and controls include:

OWASP Top 10 for LLMs
  • LLM01:2026 Prompt Injection
  • LLM02:2026 Sensitive Information Disclosure
  • LLM03:2026 Excessive Agency
  • LLM04:2026 Supply Chain
  • LLM05:2026 Data and Model Poisoning
  • LLM06:2026 Unbounded Consumption
  • LLM07:2026 Misinformation
  • LLM08:2026 Hidden Context Exposure
  • LLM09:2026 Vector and Embedding Weaknesses
  • LLM10:2026 Improper Output Handling
OWASP Top 10 for Agentic Applications
  • ASI01: Agent Goal Hijack
  • ASI04: Agentic Supply Chain Vulnerabilities
  • ASI06: Memory & Context Poisoning
  • ASI09: Human-Agent Trust Exploitation
NIST AI Risk Management Framework
  • MAP 5.1: Likelihood and magnitude of identified impacts are documented
  • MEASURE 1.1: Risk measurement approaches and metrics are selected and implemented
  • MEASURE 2.7: AI system security and resilience are evaluated and documented
  • MANAGE 1.1: Determine whether deployment should proceed