Stop Prompt Injection Threats

Use Case

Prompt injection can manipulate AI systems into ignoring intended instructions, disclosing data, or taking unauthorized actions. PointGuard AI combines runtime guardrails with adversarial testing to identify these attacks before they become business-impacting events.

Challenges

The AI environment creates several recurring security and governance challenges:

• Malicious instructions can override intended system behavior
• Jailbreaks can bypass safeguards and expose sensitive information
• Compromised prompts can trigger unauthorized agent actions
• Conventional signatures cannot interpret model-context attacks

Solution

PointGuard AI provides a comprehensive solution that combines inline runtime inspection, adversarial testing, and policy enforcement:

1. Inspect prompts inline. Use AI Intelligent Guardrails to analyze AI prompts as they enter the application to detect prompt injection, jailbreak attempts, and malicious instructions.

2. Evaluate model responses. Inspect outputs for unsafe behavior, policy violations, malicious code, or evidence that injected instructions influenced the model.

3. Block malicious interactions. Apply policy-based enforcement to stop unsafe prompts or responses before they reach users, tools, or downstream systems.

4. Test defenses continuously. Use automated adversarial testing to identify prompt-injection weaknesses and validate that controls remain effective as applications change.

Combining runtime enforcement with proactive testing reduces the chance that malicious instructions can redirect AI behavior or create a path to sensitive systems.

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
• LLM10:2026 Improper Output Handling

OWASP Top 10 for Agentic Applications

• ASI01: Agent Goal Hijack
• ASI02: Tool Misuse and Exploitation
• ASI03: Identity and Privilege Abuse
• ASI05: Unexpected Code Execution (RCE)
• ASI10: Rogue Agents

NIST AI Risk Management Framework

• GOVERN 1.4: Transparent risk policies, procedures, and controls are established
• MAP 1.1: Intended use, context, users, and lifecycle risks are documented
• MEASURE 2.7: AI system security and resilience are evaluated and documented
• MANAGE 4.1: Post-deployment monitoring and change management are implemented