For SaaS teams shipping AI, MCP, and agents

Security testing for SaaS products that ship AI, MCP, and agents

Appsecco tests core product behavior, connected infrastructure, and AI/MCP/agent attack surfaces together, so the coverage matches the system you actually shipped.

Fixed quote, project window, report walkthrough, and one revalidation window included.

10+
Years in product security
150+
Organizations secured
5,000+
Security vulnerabilities discovered
700+
Security engagements

Trusted by product teams at

SaaS App/APIs built for humans

Product behavior, roles, tenants, and workflows

For app logic, API trust boundaries, tenant isolation, and multi-role workflows used directly by people.

Infra powering your SaaS

Cloud, Kubernetes, IAM, and connected trust paths

For infrastructure risk behind the product: identities, workloads, clusters, storage, exposed services, and escalation paths.

MCP & AI integrated in the app

AI/MCP builders start here

Prompts, tools, retrieval, resources, and product actions

For chatbots, copilots, MCP servers, retrieval, tool calls, and AI features wired into product workflows.

Autonomous AI agents

Planning loops, memory, tools, approval gates, and actions

For systems that chain steps, invoke tools, write state, or make decisions across product and internal boundaries.

Not sure which one you are building? Start with the highest-impact action the system can take. We will shape the final scope during the technical sync.

MCP & AI in app

AI changes what prompts can reach and what tools can do

What routine testing often covers

A completed app/API pentest may never exercise prompt-to-tool paths, retrieval access, MCP resources, or model-mediated actions.

What can remain untested

The AI layer can expose tenant data, call internal tools, cross role boundaries, or trigger workflows the original test never simulated.

How Appsecco changes the plan

We use your prompts, tools, resources, auth flows, and data paths to build AI/MCP-specific hypotheses before human testers validate what is exploitable.

Tailored methodology

A test plan built around how your product can actually fail

The benefit is sharper coverage: discovery, AI-assisted hypothesis generation, Appsecco's internal KB, and human tester judgment turn your implementation details into tests that matter.

1
Discovery turns product context into scope
We start with roles, tenant boundaries, APIs, cloud touchpoints, AI/MCP behavior, agent actions, data flows, release context, and what has already been tested.
2
AI helps surface implementation-specific hypotheses
AI-assisted analysis and Appsecco's internal KB help identify where this kind of system usually breaks, then adapt those ideas to your exact implementation.
3
Human testers validate the real gaps
Our testers use repeatable tooling and manual attack work to prove what is reachable, exploitable, chainable, and worth fixing.
4
Report and revalidation close the loop
You get findings, attack paths, fix guidance, and revalidation evidence connected to the product behavior we actually tested.

Sample report

The report is designed for engineering review, not just procurement

Before any engagement, you can inspect the same evidence standard we use in client work: scoped findings, attack-path narrative, remediation guidance, and supporting artifacts that stand up in internal review.

Executive summary tied to decisions, not only severity labels
Attack-path narrative that shows how issues combine into real risk
Remediation guidance your engineers can act on without translation
Artifacts that make revalidation and customer review easier

Public research

The practice publishes real tooling, not only service copy

Appsecco's research footprint is visible before the first call. You can evaluate technical depth from published tools and research, not just service descriptions.

Published by the Appsecco research team and maintained as public technical assets.

What your team gets

The deliverable system is built for triage, remediation, and proof

A useful report has to move between engineering, security, and review stakeholders without losing context.

Executive summary
Risk themes, business impact, and the decisions leadership should make first.
Evidence-backed findings
Reproduction steps, impact notes, and supporting artifacts tied to the real attack path.
Fix guidance
Practical remediation direction with enough specificity for engineering follow-through.
Revalidation proof
A clear record of what was retested when customers, auditors, or launch reviewers ask for closure.

Additional references are available under NDA when the buying process calls for them.

Engagement: Project engagement
Delivery: Start date, end date, report, revalidation
Commercial model: Fixed quote

Attack plan shaped by discovery and implementation-specific hypotheses
Human testing across app/API, cloud/IAM, AI/MCP, and agent surfaces in scope
Report walkthrough and one revalidation window included

Scope the assessment

Engagement: Recurring cadence
Delivery: Monthly review of changed surface
Commercial model: Scoped after baseline

Good fit for enterprise SaaS and AI/MCP products with frequent releases
Focuses on changed API routes, roles, tools, integrations, and workflows
Keeps follow-up testing tied to what changed since the last baseline

Discuss monthly scans

When AI can reach tools and actions

Know what your product can expose before attackers do.

AI features, MCP servers, and autonomous agents introduce new paths to data, roles, tools, and internal actions. Tell us what you shipped and we will map the testing that matches the stakes.

Request scoped assessment

or Only test AI/MCP/Agent first

No sales pressure

Fixed quote before testing

Project report and revalidation included

Core product surfaces

AI-enabled product surfaces

Product security specialists, not checkbox pentesters.

Company

Learn

Compliance

Industries

Security testing for SaaS products that ship AI, MCP, and agents

Pick the path that matches what you shipped

Product behavior, roles, tenants, and workflows

Cloud, Kubernetes, IAM, and connected trust paths

Prompts, tools, retrieval, resources, and product actions

Planning loops, memory, tools, approval gates, and actions

The missing tests change with the system you shipped

Human product paths fail where roles, tenants, and workflows meet

Infrastructure risk appears when identity can move

AI changes what prompts can reach and what tools can do

Agents add decision-making and action authority

A test plan built around how your product can actually fail

Discovery turns product context into scope

AI helps surface implementation-specific hypotheses

Human testers validate the real gaps

Report and revalidation close the loop

Inspect the report standard, research trail, and review package before the first call

The report is designed for engineering review, not just procurement

Start with the full assessment. Add monthly scans when shipping keeps moving.

Comprehensive product security + AI/MCP/agent assessment

Monthly scans for fast release cycles

Know what your product can expose before attackers do.