Results for “intrusion-prevention”

Restricts unauthorized removable media access on endpoints by implementing USB device control policies via Group Policy, Intune, or EDR platforms to prevent data exfiltration and malware introduction.

Detects and responds to OAuth token theft and replay attacks in cloud environments, focusing on Microsoft Entra ID token protection, conditional access policies, and sign-in anomaly detection.

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts using pandas for statistical analysis and anomaly detection.

Deploys DNS, HTTP, and AWS API key canary tokens across network infrastructure to detect unauthorized access and lateral movement, with webhook alerting to Slack, Teams, email, or generic HTTP endpoints.

Detect container escape attempts using runtime security tools like Falco, Sysdig, and custom seccomp/audit rules.

Detect container escape attempts using Falco, seccomp, and auditd, with rules for privileged containers, Docker socket access, and kernel module loading.

Security controls for RAG. Indirect prompt-injection via retrieved documents, PII detection/redaction (Microsoft Presidio, AWS Comprehend), multi-tenant isolation, ACL-aware retrieval with row-level/metadata filtering, data-leakage prevention, jailbreak hardening on retrieved context, GDPR right-to-be-forgotten in vector DBs. USE WHEN: user mentions "prompt injection RAG", "indirect prompt injection", "PII redaction", "Presidio", "ACL RAG", "row-level security", "multi-tenant RAG isolation", "GDPR vector DB", "right to be forgotten", "jailbreak", "data leakage RAG" DO NOT USE FOR: hallucination detection - use `rag-guardrails`; tenancy scaling patterns - use `rag-production`; audit tracing schema - use `rag-observability`

Builds input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs using NeMo Guardrails, Presidio, and Guardrails AI.

Deploy Llama Guard, NeMo Guardrails, and LLM Guard as runtime input/output scanners to block jailbreaks, prompt injection, and toxic content in production LLM applications.

Prevents destructive operations on production systems and restricts file edits to a specific directory when running agents autonomously.

Detects and prevents code injection attacks targeting serverless functions through static analysis, event source poisoning detection, and IAM policy auditing.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.