They're in production with permissions nobody documented, and your app pentest never looked inside the agent. We red-team yours in 15 minutes and hand you one report your auditor, CISO and board can each read.
Agent runtime scan
Prompt injection
42 attempts
Tool hijacking
19 paths
Credential exposure
7 secrets
Data egress
14 routes
You gave agents real authority across your stack, faster than anyone documented it. A standard pentest checks the app boundary, not what the agent does inside it. SIEM and APM were not built to read autonomous behaviour either. So a leaked credential or an injected prompt does not trip an alarm. It surfaces as an outage or an audit finding, and by then it's on record.
AI agent pentesting tests what an autonomous agent can actually do inside your boundary, not just the app around it. Two checks cover the same risk surface.
Before an agent ships, we red-team it: 21 scanners across 22 attack categories, from tool discovery and prompt extraction to SSRF, sandbox escape and privilege escalation. Every failure is reproduced, scored, and mapped to your frameworks. In one recent EarlyCore engagement, attack success across the 629 scenarios run dropped from 80% to 23.5% after the fixes. You fix, we re-test, then go live with proof instead of hope.
Agents in production don't stay still. New prompts, new tools, a swapped model, and the risk surface shifts. We sit a lightweight layer (SDK or sidecar) beside the runtime that captures every LLM call, tool use and credential touch. The scanner suite re-runs on every change, EarlyCore detectors flag prompt injection and data egress live, and alerts land in Slack or your SIEM.
We secure agents from our AI for Business and AI for Code builds, and agents you have shipped yourself.
This is a real read-only EarlyCore scan against a public LLM-based assistant. Severity breakdown, framework coverage, and every blocking issue with its evidence and a recommended fix. It's the same artefact your auditor reviews and your board signs off, so you can judge the depth for yourself before a single call.
Scope the agent on a 30-minute call. We agree what we're testing and what counts as a fail.
We run the adversarial scenarios in 15 minutes. No code changes, nothing taken offline.
You get a findings report mapped to your compliance and security frameworks, with each issue reproduced.
Fix the gaps, re-test to confirm, then move to continuous monitoring so the next change doesn't reopen them.
Your auditor, your CISO, and your engineers all open the same file and each find their own language. Every finding maps to the framework that team already reports against.
EU Artificial Intelligence Act compliance testing
General Data Protection Regulation compliance testing
Digital Operational Resilience Act testing for ICT and third-party AI controls
Article 21 evidence for essential and important entities
AI Management System requirements
AI Risk Management Framework compliance testing
LLM-specific vulnerabilities
API security coverage for AI endpoints
threats and mitigations for agent systems
adversarial threat landscape for AI systems
access control and data-retrieval edge cases
tests for MCP-based systems
The first scan runs in 15 minutes with no code changes, and you see the findings before you commit. The incident, if it comes first, won't be that polite.