AI / LLM Security Testing in Hyderabad · HITEC City
OWASP LLM Top 10 red-teaming from our HITEC City hub for the pharma, GCC and government AI systems being built across Hyderabad and South India.
What is AI Pentest in Hyderabad?
AI penetration testing assesses applications built on large language models for prompt injection, insecure output handling, data leakage, and agent/tool abuse, following the OWASP Top 10 for LLM Applications. In Hyderabad, Macksofy scopes and delivers the engagement to local regulators, procurement, and timelines — with proof-of-concept findings, board-ready reporting, and a remediation retest.
How a Macksofy ai pentest engagement runs in Hyderabad.
Hyderabad has quietly become one of India's densest AI-engineering clusters — the GCC AI teams inside HITEC City and the Financial District, the ML pipelines behind pharma drug-discovery and clinical operations, and the state's own AI push through T-Hub and the Telangana AI mission. Macksofy runs its South India regional hub out of HITEC City, Madhapur, which means AI/LLM security testing here comes with local senior consultants who can be onsite within two hours across the city — not a fly-in-only model. We test the model, the RAG pipeline, the agent tools and the training-data supply chain, all mapped to the OWASP LLM Top 10 and MITRE ATLAS.
Pharma is Hyderabad's distinctive AI risk surface. Life-sciences companies here are wiring LLMs into clinical documentation, pharmacovigilance triage, regulatory-submission drafting and R&D knowledge retrieval — pipelines that ingest patient data, trial data and proprietary molecule research. Our engagement probes membership inference and model inversion to see whether a fine-tuned model leaks the trial or patient records it was trained on, tests RAG assistants for retrieval of documents the querying user was never cleared to see, and treats PII/PHI leakage as the compliance event it is under the DPDP Act and, for US-facing clinical work, HIPAA.
GCC AI teams in Hyderabad carry a parent company's governance program on their shoulders — Microsoft, Amazon, Google, Qualcomm and dozens of mid-size US/EU centres run real AI engineering here. For those teams we format the engagement so the India centre's evidence maps straight into the parent's NIST AI RMF, ISO/IEC 42001 or EU AI Act high-risk-system controls: a threat model against OWASP LLM Top 10 and ATLAS, per-finding reproducer prompts, and a control-mapping annex the group CISO's team can attest against without a translation layer.
The attack chain is the same rigour we bring everywhere: prompt injection into tool-call abuse into data exfiltration. A crafted document in the RAG corpus or a poisoned support ticket reaches the model as instructions; an agent is steered into invoking a privileged tool on the user's behalf; and sensitive context or system-prompt secrets leak through an output channel the guardrail never inspected. We run the full jailbreak sweep — roleplay, encoding, multi-turn, system-prompt extraction — and test output-format hijacks that smuggle links, markdown and tool-schema injections past naive filters.
RAG and multi-tenancy get direct scrutiny. Cross-tenant retrieval leakage — one user or tenant surfacing another's embedded documents because access control lives in app code the LLM path bypasses — is a failure we test at the vector store's own authorization layer, alongside embedding-space adversarial queries and indirect injection via poisoned corpus documents. For agentic and function-calling systems we fuzz function-call schemas, test confused-deputy tool abuse, enumerate exposed MCP servers, and attempt sandbox escape from code-execution tools.
The supply chain underneath the model is in scope by default: provenance of HuggingFace weights and third-party embedding models, training-data poisoning vectors in fine-tune pipelines, and cost-amplification (long-context and tool-loop) attacks that turn inference spend into a denial-of-wallet problem. For government and government-adjacent AI projects coming out of the Telangana ecosystem, we add the data-sovereignty and audit-trail expectations those engagements carry, and keep the evidence in a form a state audit can accept.
As with every Macksofy AI engagement, we co-build the guardrails rather than just naming the gaps. Each exploitable finding ships with a suggested guardrail prompt, an output-validator rule (PII/PHI, secrets, prompt-leak, toxicity) and a sandboxing or rate-limit pattern the platform team can deploy the same week — with a free guardrail retest inside 30 days built into the base statement of work. Reporting is Mumbai-supported so the HITEC City delivery team pairs local onsite presence with the senior review bench behind every South India engagement.
Five phases. Hyderabad timeline.
Every Macksofy ai pentest engagement in Hyderabad runs through the same phased protocol — adapted to Hyderabad-specific procurement, regulator and delivery realities.
- Phase 01
Threat model & scope
Week 1- Architecture and data-flow review across model, RAG, agent tools and fine-tune pipeline
- Sensitivity mapping of training/retrieval corpora — patient, trial, R&D and PII data
- Threat model aligned to OWASP LLM Top 10 + MITRE ATLAS and the client's governance framework
- Rules of engagement and a safe test dataset so probing never touches real PHI/PII
- Phase 02
Prompt injection & jailbreak
Weeks 1–2- Direct and indirect (RAG-borne) prompt injection across every LLM entry point
- Jailbreak sweep — roleplay, encoding, multi-turn, system-prompt extraction
- Output-format hijack (markdown, link, image, tool-schema injection)
- Phase 03
RAG, PHI leakage & multi-tenancy
Weeks 2–3- Cross-tenant / cross-clearance retrieval leakage against the vector store's authorization
- Membership-inference and model-inversion testing for training-data leakage (PII/PHI)
- Embedding-space adversarial queries and poisoned-document indirect injection
- Phase 04
Agentic & supply chain
Weeks 3–4- Function-call schema fuzzing and confused-deputy tool-abuse testing
- MCP server enumeration/abuse and code-execution sandbox-escape attempts
- Model/embedding provenance and training-data poisoning review
- Phase 05
Guardrail co-build & retest
Weeks 4–5- Per-finding reproducer prompt, guardrail prompt and PII/PHI output-validator rule
- Output-classifier coverage and cost-amplification / denial-of-wallet testing
- Free guardrail retest within 30 days against the deployed fix
- Phase 01Week 1
Threat model & scope
- Architecture and data-flow review across model, RAG, agent tools and fine-tune pipeline
- Sensitivity mapping of training/retrieval corpora — patient, trial, R&D and PII data
- Threat model aligned to OWASP LLM Top 10 + MITRE ATLAS and the client's governance framework
- Rules of engagement and a safe test dataset so probing never touches real PHI/PII
- Phase 02Weeks 1–2
Prompt injection & jailbreak
- Direct and indirect (RAG-borne) prompt injection across every LLM entry point
- Jailbreak sweep — roleplay, encoding, multi-turn, system-prompt extraction
- Output-format hijack (markdown, link, image, tool-schema injection)
- Phase 03Weeks 2–3
RAG, PHI leakage & multi-tenancy
- Cross-tenant / cross-clearance retrieval leakage against the vector store's authorization
- Membership-inference and model-inversion testing for training-data leakage (PII/PHI)
- Embedding-space adversarial queries and poisoned-document indirect injection
- Phase 04Weeks 3–4
Agentic & supply chain
- Function-call schema fuzzing and confused-deputy tool-abuse testing
- MCP server enumeration/abuse and code-execution sandbox-escape attempts
- Model/embedding provenance and training-data poisoning review
- Phase 05Weeks 4–5
Guardrail co-build & retest
- Per-finding reproducer prompt, guardrail prompt and PII/PHI output-validator rule
- Output-classifier coverage and cost-amplification / denial-of-wallet testing
- Free guardrail retest within 30 days against the deployed fix
Which Hyderabad verticals we deliver AI Pentest for.
Pharma & life sciences
LLMs in clinical documentation, pharmacovigilance, regulatory drafting and R&D retrieval — models trained on trial and patient data where PHI leakage is a compliance event.
GCC (US + EU enterprise)
AI engineering built in HITEC City against a parent's NIST AI RMF / ISO 42001 / EU AI Act governance program.
Government & public sector
Telangana-ecosystem AI projects with data-sovereignty, audit-trail and state-audit evidence expectations.
SaaS & fintech
Product AI features and multi-tenant RAG assistants shipped from Hyderabad's growing product base.
The Hyderabad deliverable pack.
Every Hyderabad ai pentest engagement closes with the pack below — regulator-ready evidence, technical detail and board-readable summaries.
- AI/LLM threat model mapped to OWASP LLM Top 10 + MITRE ATLAS
- Per-finding writeup with a working reproducer prompt and severity rating
- Training-data leakage assessment (membership inference, model inversion, PII/PHI probing)
- Multi-tenant / cross-clearance RAG leakage findings at the vector-store layer
- Agentic tool-abuse and MCP/sandbox findings for function-calling systems
- Guardrail prompts + PII/PHI output-validator rules per exploitable finding
- Control-mapping annex for NIST AI RMF / ISO 42001 / EU AI Act / DPDP
- Free guardrail retest report within 30 days
A Hyderabad ai pentest case study.
AI/LLM assessment of a RAG assistant over clinical and R&D documentation, plus membership-inference testing on the fine-tuned retrieval model; OWASP LLM Top 10 + MITRE ATLAS with guardrail co-build
A retrieval path exposing documents outside the querying researcher's clearance was proven and closed; membership-inference testing confirmed the fine-tune did not leak identifiable trial records after a corpus-scoping fix; PHI output-validator rules deployed and retested within 30 days, with a DPDP/HIPAA control-mapping annex for the compliance team.
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