Results for “prompt-injection-defense”

Detect and defend against prompt injection hidden in documents, web pages, and images consumed by an agent.

Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.

Detects prompt injection attacks targeting LLM-based applications using regex pattern matching, heuristic scoring, and DeBERTa transformer classification.

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.

Wire Promptfoo and DeepTeam into CI/CD for automated regression red-teaming of LLM apps against OWASP LLM Top 10 and OWASP Agentic presets, failing the build when jailbreak or injection vulnerabilities regress.

Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Reverse-engineers EDR, Defender, and AV hook tables, ETW providers, and AMSI implementations to build targeted bypasses including unhooking, indirect syscalls, ETW patching, and call stack spoofing for authorized red team operations.

Authorized OT/ICS security assessment covering Purdue model zoning, PLC/SCADA exposure, industrial protocol discovery, and safe passive-first evaluation.

Guides authorized Active Directory security research covering Kerberos attacks, AD CS vulnerabilities, BloodHound path analysis, NTLM relay, and domain privilege escalation techniques.

Provides a comprehensive penetration testing toolchain with 20+ security tools (Nmap, Nuclei, SQLMap, FFUF, Hashcat, etc.) exposed via MCP servers for authorized vulnerability scanning, exploitation, and reporting.

Conduct authorized security assessments of LLM applications and AI agents, covering prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.