New Service — AI Security
AI Security
LLMs, GenAI, and ML systems need security too.
As organizations deploy AI systems into production, a new and poorly understood attack surface emerges. Traditional security tools weren't built for it. APPSECREW's AI Security practice brings offensive security rigor to the frontier of machine intelligence — finding vulnerabilities in LLM applications, agentic workflows, and ML pipelines before adversaries exploit them.
Our Approach
AI Security Assessment Methodology
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AI System Mapping & Threat Modeling
We map your entire AI attack surface — LLM providers, prompt chains, retrieval systems (RAG), agent workflows, tool integrations, and data pipelines — building a comprehensive threat model.
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Prompt Injection & Jailbreak Testing
Systematic testing of your LLM application's resilience to direct and indirect prompt injection, jailbreaking, role-play attacks, and instruction override techniques.
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Output & Behavior Analysis
Evaluating what your AI system can be manipulated into producing — PII leakage, system prompt extraction, insecure output rendering, and model inversion attacks.
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Agentic AI & Tool Abuse Testing
If your AI system uses tools (web search, code execution, file systems, APIs) — we test for tool misuse, privilege escalation through agent actions, and autonomous decision manipulation.
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ML Pipeline & Infrastructure Audit
Security review of your ML training pipeline, model registry, dataset handling, and deployment infrastructure — including data poisoning vectors and model supply chain risks.
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Findings Report & AI Security Roadmap
A specialized AI security report with OWASP LLM Top 10 mapping, risk-prioritized findings, and a practical roadmap for building security into your AI development lifecycle.
What We Cover
Full Coverage, Zero Gaps.
LLM Application Security
- Prompt Injection (Direct & Indirect)
- Jailbreaking & Filter Bypass
- System Prompt Extraction
- Training Data Leakage
- Model Denial of Service
Agentic AI Testing
- Tool & Plugin Abuse
- Agent Privilege Escalation
- Multi-Agent Trust Boundaries
- Autonomous Decision Manipulation
- Callback & Webhook Injection
RAG & Knowledge Base
- Knowledge Poisoning
- Document Injection via RAG
- Retrieval Manipulation
- Vector DB Security
- Embedding Inversion
ML Pipeline Security
- Training Data Poisoning
- Model Theft & Extraction
- Adversarial Examples
- Model Supply Chain
- MLOps Infrastructure Review
AI Infrastructure
- API Key & Model Access Control
- Rate Limiting & Abuse
- Logging & Observability
- Model Version Control
- Inference Endpoint Security
Governance & Compliance
- AI Risk Framework
- EU AI Act Alignment
- Bias & Fairness Review
- PII in Training Data
- AI Incident Response Planning
What You Get
Clear, Actionable Deliverables.
AI Security Report
Findings mapped to OWASP LLM Top 10 with severity ratings, reproduction steps, and remediation guidance specific to AI systems.
Threat Model Diagram
Visual map of your AI attack surface — data flows, trust boundaries, and identified threat vectors.
AI Security Roadmap
Prioritized 90-day plan for integrating security into your AI development and deployment lifecycle.
Team Enablement Session
A live session with your AI/ML team covering key vulnerabilities found, remediation patterns, and secure-by-design practices for AI.
Who It's For
Built for Organizations That Take Security Seriously.
- SaaS and product companies building LLM-powered features or chatbots
- Enterprises deploying internal AI assistants with access to sensitive data
- Organizations building autonomous AI agents with tool-use capabilities
- ML engineering teams operating training pipelines and model registries
- Companies subject to EU AI Act or sector-specific AI governance requirements
- Any team that has shipped an AI product and hasn't assessed its security posture
Ready to get started?
Every engagement starts with a free scoping call. No obligations — just an honest conversation about your security posture.
Book a Free Call contact-crew@appsecrew.comExplore More