Adversarial Testing forAI Systems, LLMs &Agentic Pipelines
Purpose-built penetration testing for AI applications — covering prompt injection, jailbreaking, RAG poisoning, MCP tool abuse, and autonomous agent exploitation across your entire AI attack surface.
Prompt Injection Lab
Live adversarial simulation engine demonstrating real attack chains executed against production AI deployments. Each scenario replicates actual techniques from Spakto's engagement library.
// ─── Adversarial AI Testing Environment ───────────────────────────────── //
// All simulations are replicas of real engagement findings. No AI was
// harmed in the production of this pentest lab.
spakto@lab:~$ █
OWASP LLM Top 10 Coverage
Complete adversarial coverage of all 10 OWASP LLM Application Security risks — mapped to MITRE ATLAS techniques, scored with test case counts and average findings per engagement.
Agent Kill Chain
End-to-end agentic attack path: from a single malicious user prompt to full infrastructure compromise via autonomous tool chaining. Click any node to inspect.
Frequently Asked Questions
Frequently asked
questions.
answered
AI systems introduce unique attack surfaces — prompt injection, model extraction, jailbreaking, and data poisoning — that traditional security testing methodologies were never designed to address. Spakto's AI pentest methodology tests the full AI pipeline: model inputs, outputs, RAG pipelines, agentic tool calls, and API boundaries.
Indirect prompt injection occurs when malicious instructions are embedded in content the AI retrieves or processes — documents, web pages, emails — rather than entered directly by a user. This allows attackers to hijack AI agents, exfiltrate data, or perform unauthorised actions without any direct user interaction.
Engagements deliver a risk-prioritised findings report with CVSS scoring, step-by-step attack reproduction, business impact assessment, and remediation guidance specific to your AI architecture — including both immediate fixes and architectural hardening recommendations.
Yes. Black-box attacks against AI models are highly effective. Prompt injection, indirect prompt injection via RAG retrieval, and adversarial inputs can all manipulate model behavior through the user interface alone, without any code access.
We enumerate all tool schemas and function call boundaries, then test for privilege escalation through chained tool calls, data exfiltration via tool outputs, and logic manipulation through adversarial prompts that cause agents to take unintended actions.