AI Agent Permission and Tool-Use Security Review

• CISO
• Head of Platform Engineering
• Head of Payment Operations
• AI and ML Engineering Lead

Payment companies are giving agents real permissions: issuing refunds under a limit, updating merchant records, querying ledgers, triaging disputes, or acting through MCP servers wired to internal systems. The risk moves from what the model says to what it does. One over-scoped service account or missing approval step can turn a prompt injection into a payout.

We review your agents the way an attacker and an auditor would. First we build an inventory: every agent, every tool and MCP server it can call, the credentials behind each tool and the actions those credentials allow, including any that move money or change payout details. We compare this with what each agent actually needs and mark the excess. Then we test in staging: planting instructions in tickets, emails and data fields, attempting cross-merchant actions, chaining tools and pushing past limits. You get a permission matrix, validated findings with tool-call traces, and concrete controls such as scoped tokens, transaction limits enforced outside the model, allowlists and human approval on high-impact actions.

• Inventory of agents, tools, MCP servers, credentials and reachable actions
• Least-privilege gap analysis per agent and per tool
• Review of approval gates, limits and kill switches on money-moving and account-changing actions
• Adversarial testing for goal hijack, tool misuse and indirect prompt injection in staging
• Audit log review covering agent identity, on-behalf-of user, inputs and tool calls
• Control recommendations with example policies and code-level fixes

1. 01InventoryWe document each agent, its tools and MCP servers, the credentials used and the actions each credential allows.
2. 02Mark high-impact actionsRefunds, payouts, settlement account changes, KYC status changes and bulk data exports are tagged as high-impact.
3. 03Gap analysisGranted permissions are compared with what each task needs, and missing approvals or limits are recorded.
4. 04Adversarial testingIn staging, we attempt goal hijack, cross-merchant actions and injected instructions through realistic data channels.
5. 05Validate findingsEach issue is reproduced and rated by impact, with the tool-call trace as evidence.
6. 06Controls and retestWe recommend specific controls, review your fixes and retest the high-impact paths.

1. Discovery
   • Agent and MCP server inventory
   • Credential and scope export from IAM and secrets manager
   • Action catalogue per tool
2. Analysis
   • Permission matrix builder
   • High-impact action map
   • Approval and limit checks
3. Adversarial testing
   • Goal hijack and tool misuse playbooks
   • Injection seeding through tickets, emails and records
   • Staging sandbox with test merchants
4. Evidence and remediation
   • Tool-call trace capture
   • Finding validation and severity rating
   • Control recommendations and retest tracker

Our testers validate every finding before it is reported. Your security, engineering and payment operations leads decide which controls to apply, and we recommend human approval on any action that moves money or changes payout details.

• Agent frameworks such as LangGraph, OpenAI Agents SDK or custom orchestrators
• MCP servers and internal tool APIs
• Cloud IAM and secrets managers on AWS, Azure, GCP or Vault
• Logging and SIEM such as Splunk, Elastic or Datadog
• Jira for findings and remediation tracking

• Testing only in staging with test merchants and synthetic balances
• No real refunds, payouts or account changes triggered at any point
• Read-only access to IAM and configuration during discovery
• Written stop conditions for any unexpected effect outside staging
• Evidence encrypted and deleted at the end of the retention period

• Supports the accountability and human oversight practices recommended in RBI's FREE-AI framework.
• Findings map to excessive agency and related risks in the OWASP Top 10 for LLM Applications.

• Fintech & Payments

  Pre-Launch Security Assessment for LLM Features

  Test a new chatbot, copilot or LLM feature for prompt injection, cross-customer data leakage, off-policy answers and unsafe output handling before release, with evidence and a retest.

• Fintech & Payments

  DPDP-Aligned AI Data Handling Assessment

  Map where personal data flows through your AI features, prompts, logs, vector stores and vendors, and get a gap register and evidence pack against DPDP Act duties for your DPO.

• SaaS & AI Product Companies

  AI Agent Red-Teaming (Tool Misuse and Goal Hijack)

  Red-team your AI agents for goal hijacking, tool misuse, excessive agency, privilege abuse and memory poisoning, in a sandbox that records every tool call and decision.

• Banking

  AI Governance Evidence Pack for Banks

  Inventory the bank's AI and ML systems and assemble the evidence boards, auditors and supervisors ask for: purpose, data, testing, monitoring, incidents and accountable owners.