AGENTIC AI SECURITY

We break your AI agents
before someone else does.

OWASP-aligned security assessments for agentic AI systems. Architecture review. Red team testing. Prioritised remediation.

AI agents don't just talk. They act.

AI agents are moving from proof-of-concept to production. They plan autonomously, call tools, access databases, coordinate with other agents, and interact with external APIs. This fundamentally changes the security landscape.

Traditional application security testing doesn't cover the new attack surfaces that agentic systems introduce. The risk is no longer just about what an AI says. It's about what an AI does.

The OWASP Top 10 for LLM Applications and the emerging AIVSS have begun codifying these threats. But frameworks alone don't find vulnerabilities. Hands-on red teaming does.

$ prebreach scan --target agent-config.json
[*] Analysing agent architecture...
[*] Mapping trust boundaries...
[*] Testing configuration layer...
[!] Config file accepts unvalidated input
[✗] VULN: Prompt injection via config
Severity: HIGH (AIVSS 7.5)
OWASP: LLM01 — Prompt Injection
[*] Testing tool access controls...
[!] Agent executes tools without scope validation
[✗] VULN: Excessive agency via tool misuse
Severity: HIGH (AIVSS 7.2)
[*] Testing agent-to-agent delegation...
[✓] Delegation boundaries enforced
 
Scan complete: 2 vulnerabilities found
$ _

Full-spectrum agentic threat coverage

Every assessment covers the attack surfaces defined by OWASP and emerging industry standards for agentic AI systems.

LLM01: PROMPT INJECTION

Prompt Injection

Direct and indirect attempts to override agent instructions through user inputs, configuration files, context windows, and data sources the agent consumes.

LLM07: INSECURE PLUGIN DESIGN

Tool Misuse & Insecure Plugins

Testing whether agents can be manipulated into using their tools (APIs, code interpreters, database access, MCP servers) in unintended ways.

LLM06: EXCESSIVE AGENCY

Access Control & Permissions

Verifying that agents respect user-level permissions and don't escalate access. Testing whether shared service accounts allow cross-user data retrieval.

LLM05 / LLM01

Configuration & Context Manipulation

Attacking the agent's configuration layer (config files, environment variables, system prompts, and RAG pipelines) to alter behaviour without direct prompt access.

LLM09: OVERRELIANCE

Agent-to-Agent Exploitation

In multi-agent architectures, testing whether compromising one agent cascades to others. Targeting orchestrator agents to manipulate delegation logic.

LLM02: SENSITIVE INFO DISCLOSURE

Data Exfiltration

Testing whether agents can be tricked into revealing training data, system prompts, API keys, user data, or other sensitive information through their responses or tool outputs.

Three phases. No guesswork.

Every engagement follows a structured methodology designed to find real vulnerabilities and deliver actionable fixes.

01

Architecture Review

We map your agent architecture, trust boundaries, data flows, and tool integrations to build a threat model before testing begins.

  • Agent architecture mapping
  • Trust boundary identification
  • Data flow analysis
  • Tool & API inventory
  • Permission model review
02

Red Team Testing

Hands-on exploitation against every identified attack surface. We try to break your agents using the same techniques a threat actor would.

  • Prompt injection testing
  • Tool misuse exploitation
  • Permission escalation attempts
  • Data exfiltration probes
  • Multi-agent cascade attacks
03

Report & Remediation

A prioritised remediation plan with specific, actionable fixes — not generic framework advice. Your team knows what to change and when.

  • Executive summary for leadership
  • Detailed findings with evidence
  • OWASP & AIVSS severity scoring
  • Prioritised fix recommendations
  • Exploit reproduction steps

What you receive

Every assessment delivers a comprehensive security report designed for both leadership and engineering teams.

Executive Summary

Overall risk posture, key findings, and recommended priorities — written for CISOs and leadership.

Detailed Findings

Each vulnerability with description, attack vector, evidence, OWASP mapping, AIVSS score, and business impact.

Prioritised Remediation Plan

Specific, actionable fixes ranked by severity and effort. Practical changes your team can make this week, not generic advice.

Architecture Threat Map

Visual map of your agent architecture with attack surfaces highlighted and trust boundary analysis.

Exploit Documentation

Full reproduction steps for every successful exploit so your team can verify fixes independently.

Walkthrough Session

Optional call to walk through findings, answer questions, and help prioritise remediation with your engineering team.

Transparent pricing

All prices based on a day rate of £750. Enterprise engagements scoped to system complexity.

Starter
£2,250
3 DAYS
  • Single-agent architecture
  • Architecture review
  • Core red team testing
  • Written report with findings
  • Prioritised remediation plan
GET STARTED
Enterprise
Scoped
CUSTOM
  • Large-scale multi-agent systems
  • Complex tool & API integrations
  • Cross-system threat modelling
  • Ongoing assessment programme
  • Single point of contact throughout
  • Executive briefing session
DISCUSS SCOPE

Open-Source Exploit Library

Real attack patterns we've discovered, documented with reproduction steps, OWASP mappings, and mitigations. Public proof of what we find — not theoretical framework checklists.

VIEW ON GITHUB →

Practitioner, not theorist.

Prebreach is the agentic AI security practice of Maypole Digital, a consultancy founded by Andy Lith.

Andy brings 25+ years of software development experience spanning security consulting, ISO 27001 compliance, architecture review, and hands-on engineering across Azure/.NET and modern AI stacks.

Prebreach was founded on a simple observation: the tools that find vulnerabilities in traditional applications don't work for agentic AI systems. The attack surfaces are different, the failure modes are different, and the testing methodology needs to be different.

Automated threat modelling can reason about agentic risks in the abstract, but finding them in real implementations requires someone who knows how to break things.

OWASP LLM TOP 10AIVSSMAESTROCYBER ESSENTIALS
25+
YEARS ENGINEERING
OWASP
ALIGNED METHODOLOGY
£750
DAY RATE
MIT
OPEN EXPLOIT LIBRARY

Ready to test your agents?

If you're building or deploying AI agents and want to understand your security posture before going to production, we'd like to hear from you.

DISCUSS AN ASSESSMENT
hello@prebreach.ai