Your AI systems face threats traditional security can't see: prompt injection, data poisoning, model theft, adversarial attacks. Shadow AI — unvetted models, unsecured agent tools, AI-generated code with hidden vulnerabilities — is expanding your attack surface faster than your security team can map it. The SECURE-AI Framework v2 covers all four attack surfaces: LLM applications, vibe-coded software, agentic systems, and the AI supply chain. Built by someone who created Achilles AI — a 210-rule static analysis scanner purpose-built for AI-generated code security.
Prompt injection attacks can make your LLM execute unintended actions or leak sensitive data — and new techniques emerge weekly. Your traditional WAF doesn't even see these attacks. Shadow AI creates vectors your security team hasn't mapped.
Your developers use Cursor, Copilot, and Claude Code daily. Every AI-generated line of code is a potential vulnerability that no one reviewed. Traditional SAST tools miss AI-specific patterns — hallucinated library calls, insecure defaults, hardcoded secrets in prompt templates.
Autonomous agents with tool access can be manipulated into exfiltrating data, escalating privileges, or executing unauthorized actions. One compromised agent with database access can cause irreversible damage before detection.
Your AI supply chain is unvetted. Poisoned models on Hugging Face, compromised datasets, and malicious AI libraries enter your stack unchecked. The same supply chain attacks that hit npm and PyPI are now targeting AI dependencies.
Four-pillar security covering the full AI attack surface. LLM applications, AI-generated code, autonomous agents, and the AI supply chain—assessed, tested, hardened, and monitored.
Map the full AI attack surface across all four pillars—LLM endpoints, vibe-coded repositories, agent tool chains, and model/data supply chain dependencies.
Red team every vector. Prompt injection and jailbreaks on LLMs. Vulnerability scanning on AI-generated code. Tool abuse and privilege escalation on agents. Provenance checks on models and datasets.
Defense in depth across all pillars: input/output guardrails for LLMs, secure coding policies for AI assistants, least-privilege tool access for agents, and verified supply chain pipelines.
Continuous monitoring across the AI stack. Detect prompt injection, flag insecure AI-generated commits, alert on unauthorized agent actions, and track supply chain integrity drift.
A four-pillar approach to AI security covering the entire AI attack surface. Combines offensive testing with defensive hardening across LLM applications, AI-generated code, autonomous agents, and the AI supply chain.
You've deployed LLMs in production, your developers use AI coding assistants daily, you're building autonomous agents, or you depend on third-party AI models and datasets. You want to find vulnerabilities across your entire AI stack before attackers do — and you need specialized AI security expertise, not generic penetration testing.
Traditional pentesting doesn't cover AI-specific attack vectors. Prompt injection, jailbreaks, training data extraction, adversarial inputs, agent tool abuse, and AI supply chain poisoning all require specialized expertise. The SECURE-AI Framework combines traditional security with deep understanding of LLM internals, agentic architectures, and AI-specific threats.
Indirect prompt injection through retrieved data. If your RAG system pulls content from external sources, attackers can embed malicious instructions in that content. Your LLM then executes those instructions, potentially leaking data or taking unauthorized actions. But increasingly, the overlooked risk is AI-generated code—developers trust Copilot output without the same scrutiny they'd give a junior developer's pull request.
Vibe coding means building software primarily through AI coding assistants—Cursor, GitHub Copilot, Claude Code. The code ships fast but carries risks: hardcoded secrets in prompt templates, insecure API defaults, missing input validation, and overly permissive configurations. AI assistants optimize for functionality, not security. We audit vibe-coded repositories to find what the AI missed.
We threat model the full agent chain—MCP tool access, multi-agent communication, and decision boundaries. Testing covers tool abuse scenarios, data exfiltration through tool responses, prompt injection via tool outputs, privilege escalation, and unauthorized actions. Think of it as penetration testing for systems that can act on their own.
The same supply chain attacks that hit npm and PyPI are coming for AI. Poisoned models on Hugging Face, tampered training datasets, and malicious AI library dependencies are real threats. We verify model provenance, validate dataset integrity, and vet every AI dependency in your stack—because one compromised model can undermine everything downstream.
We use controlled red team testing with agreed scope and rollback procedures. Testing happens in staging environments when possible. For production testing, we use techniques that probe vulnerabilities without causing actual harm—similar to ethical hacking but for AI-specific threats across all four pillars.
No system is fully secure—AI or otherwise. The goal is defense in depth across all four pillars: LLM guardrails, code review policies, agent access controls, and supply chain verification. Multiple layers of protection so that if one fails, others catch the threat. We help you achieve appropriate security for your risk profile, not theoretical perfection.
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