Red-Teaming for Bank Chatbots and Voice Agents

• Chief Information Security Officer
• Head of Digital Banking
• Head of AI and Data Science
• IT Risk and Assurance Manager

Banks are putting language models behind WhatsApp bots, website assistants and voice agents, often built by a vendor and connected to account APIs. Traditional application penetration tests do not exercise how these systems respond to manipulative conversation, and the attack surface now includes everything a customer, or an attacker posing as one, can say.

We run a structured red-team engagement against your chatbot or voice agent in a test environment you control. Supervised AI attacker agents and human testers work through multi-turn playbooks covering prompt injection, indirect injection through documents or web content, system prompt and data leakage, tool and API misuse, authentication bypass, unsafe financial advice and mis-selling. For voice agents, attacks are delivered as audio, including code-mixed speech, interruptions and background voices. Each finding records the full conversation, its impact and a PASS, RISK or FAIL verdict mapped to the OWASP Top 10 for LLM Applications, MITRE ATLAS and relevant RBI and DPDP expectations. We retest after fixes.

• Multi-turn adversarial playbooks in English, Hindi and Hinglish, with regional languages on request
• Tests for prompt injection, jailbreaks, system prompt leakage and cross-customer data exposure
• Authentication bypass and social engineering scenarios against account-linked tools
• Voice attacks delivered as audio, including barge-in, background speech and false context
• Checks for unsafe investment, loan or tax advice and mis-selling of third-party products
• Evidence pack with transcripts, recordings, verdicts and framework mappings, plus retest

1. 01Agree scope and rulesWe confirm the bot, channels, tools and test accounts in scope, and the rules of engagement with your security team.
2. 02Map the attack surfaceWe document the bot's tools, data sources, authentication steps and guardrails from configuration and probing.
3. 03Run adversarial playbooksAttacker agents and human testers run multi-turn attacks by text and voice against the test environment.
4. 04Validate findingsEvery candidate finding is reproduced and checked by a human tester before it is recorded.
5. 05Report with evidenceFindings carry transcripts, recordings, impact, verdicts and framework mappings, with remediation guidance.
6. 06Retest fixesAfter your team or vendor fixes issues, we rerun the affected playbooks and update the evidence pack.

1. Scope and setup
   • Rules of engagement and test accounts
   • Attack-surface map of tools and data sources
2. Attack generation
   • Supervisor agent selecting playbooks
   • Attacker agents for text and voice
   • Multilingual and code-mixed prompt library
3. Delivery
   • Chat connectors for web and WhatsApp test numbers
   • Audio synthesis and telephony dialler for voice agents
4. Evaluation
   • Response judges for leakage, policy and tool misuse
   • Human validation of each finding
   • Mapping to OWASP LLM Top 10, MITRE ATLAS and NIST AI RMF
5. Reporting
   • Evidence store with transcripts and recordings
   • Findings report and retest tracker

Human testers validate every finding before it enters the report, and the bank's security team approves scope and any test that touches live channels. Verdicts come with evidence so your risk committee can make its own judgement.

• Chatbot or voice agent test endpoints: web, WhatsApp test number, SIP or phone number
• Bank test environment with synthetic customer data
• Vulnerability management or GRC tool for findings
• Issue tracker used by the bot vendor
• CI pipeline for scheduled regression runs, if required

• Testing only in agreed environments with test accounts; no real customer data
• Transactional or destructive tests only with explicit written approval
• Attack prompts and recordings stored encrypted and shared on a need-to-know basis
• Every finding reproduced before reporting
• Agreed stop conditions if testing affects availability

• Findings are mapped to the OWASP Top 10 for LLM Applications, NIST AI RMF and themes in RBI's FREE-AI framework, to help the bank evidence AI risk testing.
• Helps evidence safeguards for personal data under the DPDP Act 2023; the report does not certify compliance.

• 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.

• Banking

  Multilingual Voice Agent for Banking Service Requests

  An inbound voice agent that authenticates callers and handles card blocking, statements, cheque books and status queries in Hindi, Hinglish and regional languages, with clean handover.

• NBFCs & Lending

  Conduct Testing for Collections Voice Agents

  Test collections voice agents, in-house or vendor-built, for harassment, third-party disclosure, misrepresentation and vulnerable-borrower handling in Hindi and Hinglish, with audio evidence.

• SaaS & AI Product Companies

  LLM Application Penetration Test (OWASP LLM Top 10)

  A scoped pentest of your LLM feature and the app around it: prompt injection, data disclosure, output handling, excessive agency and system-prompt leakage, with reproducible evidence.