AI & LLM security testing in India — secure your AI before adversaries do.
India's first law firm offering attorney-privilege-protected AI security testing. We probe large language models, RAG pipelines, and agentic systems for prompt injection, excessive agency, data exfiltration, and compliance gaps — mapped to the current OWASP LLM Top 10 2026, under full attorney-client privilege.
Why this OWASP edition changes how we scope engagements
For the first time, OWASP's LLM rankings are backed by real incidents, not just practitioner opinion.
The OWASP LLM Top 10 2026, published 4 August 2026, is a genuinely different kind of document from its predecessors. Every prior edition rested entirely on community consensus — hundreds of practitioners voting on which risks mattered most. This time, OWASP gathered 7,714 real-world AI security incidents from public vulnerability databases and an AI-harm database, classified 6,639 of them with enough detail to use, and weighted that incident record at 25% of the final ranking alongside the traditional 75%-weighted vote.
The result changes what "comprehensive AI security testing" actually needs to cover. Prompt Injection and Sensitive Information Disclosure held their first- and second-place rankings — no surprise there, and both remain the risks we test most exhaustively. What moved is Excessive Agency, jumping from sixth place to third, the single largest shift on the entire list. That's not a vote-only artifact either: the incident data independently confirms it. Agentic systems — those given tools, API access, file system permissions, or the ability to take autonomous action — are where real-world damage is now concentrating, not prompt injection in isolation.
Practically, this means our testing scope has shifted to match where the evidence points. Tool-permission audits, human-authorization checkpoints for consequential actions, and agent-to-agent communication boundaries now carry the same testing weight as prompt injection resistance — not because prompt injection stopped mattering, but because a system that resists injection while still handing an agent unrestricted file-system access has solved the wrong half of the problem.
Evidence, not guesswork
OWASP LLM Top 10 2026 — the current ranking we test against
The top five entries, as ranked in the 4 August 2026 edition. The red-flagged card is the one that moved the most.
Source: OWASP GenAI Security Project, Top 10 for LLM Applications 2026, published 4 August 2026. Ranking methodology: 75% practitioner vote, 25% weighted incident data from 6,639 classified real-world cases. Consult the current OWASP publication for the complete, authoritative ranking and category definitions.
What we test
AI & LLM security services covering the full modern threat landscape
From prompt injection to model theft — every attack vector mapped to the current OWASP LLM Top 10 2026 and the Agentic (ASI) Top 10.
Prompt Injection Testing
Systematic red teaming for direct and indirect prompt injection, jailbreaks, goal hijacking, cross-modal attacks, and system prompt extraction across all major model providers.
RAG Pipeline Security
End-to-end assessment of Retrieval-Augmented Generation systems — vector store poisoning, embedding manipulation, context boundary violations, and data exfiltration.
Model Inversion & Extraction
Testing for training data leakage, membership inference, model extraction via API queries, and intellectual property theft in deployed AI systems.
Agentic System Testing
Security assessment of autonomous AI agents, multi-step tool-calling chains, MCP server integrations, and agent-to-agent communication — mapped to the OWASP Agentic Top 10 alongside the LLM Top 10.
AI Supply Chain Audit
Evaluation of model provenance, fine-tuning pipeline integrity, dependency risks in ML libraries, and third-party model marketplace security.
Output Validation Testing
Testing for insecure output handling, cross-site scripting via LLM responses, SQL injection through generated queries, and unsafe code generation.
AI Data Privacy Assessment
Analysis of PII leakage, consent boundary violations, cross-tenant data exposure in multi-tenant AI systems, and DPDPA-specific data processing risks.
AI Compliance Gap Analysis
Regulatory mapping against OWASP LLM Top 10 2026, the Agentic Top 10, NIST AI RMF, EU AI Act, MeitY guidelines, and sector-specific AI regulations (RBI, SEBI, IRDAI).
Continuous AI Monitoring
Ongoing adversarial testing for SIRI Shield subscribers — quarterly red team exercises, prompt injection canary monitoring, and drift detection alerts.
Which framework covers what
LLM Top 10 vs. Agentic (ASI) Top 10 — two layers, one deployment
Most 2026 AI deployments need both frameworks tested against, not one or the other.
| Dimension | OWASP LLM Top 10 2026 | OWASP Agentic (ASI) Top 10 |
|---|---|---|
| Published | 4 August 2026 | December 2025 |
| Covers risk when the model is a... | Component — accepting input, generating output, retrieving context | Actor — calling tools, carrying memory, taking downstream actions |
| Top risk | Prompt Injection (held #1) | Tool permission and authorization failures |
| Biggest 2026 shift | Excessive Agency, #6 → #3 | Cross-references Excessive Agency directly with the LLM list |
| When it applies to you | Any LLM-powered application, chatbot, or RAG system | Any system with tool-calling, persistent memory, or multi-step autonomy |
Sources: OWASP GenAI Security Project — Top 10 for LLM Applications 2026 (4 August 2026) and Top 10 for Agentic Applications (December 2025). Both frameworks are actively maintained — verify current category definitions before relying on this summary for a specific compliance submission.
Client outcomes
Measurable results
Across LLMs, RAG pipelines, and agentic systems.
Preliminary findings delivered fast, full red team report in 10 business days.
Zero forced disclosures to regulators across engagements to date.
Every finding gets a remediation path before the engagement closes.
Our process
How we engage
Scoping & Threat Modelling
We map your AI system architecture, identify trust boundaries, and define attack scenarios based on your threat model and regulatory requirements.
Automated Recon & Probing
Automated tools enumerate model capabilities, test input/output boundaries, and identify surface-level vulnerabilities across all endpoints.
Manual Adversarial Testing
Senior engineers execute targeted attacks: prompt injection chains, context manipulation, privilege escalation, and data exfiltration attempts.
Legal & Compliance Mapping
Attorneys map every finding to applicable regulations — DPDPA, OWASP, NIST, EU AI Act — and assess liability exposure and notification obligations.
Privileged Report & Remediation
Detailed findings under attorney-client privilege with severity scoring, exploit proof-of-concept, and a prioritised remediation roadmap.
Representative matters
Typical AI security engagements
Real engagement patterns. Client details anonymised. All findings delivered under attorney-client privilege.
14 prompt injection paths found
Red-teamed an LLM-powered financial advisor chatbot. Discovered 14 prompt injection paths that could extract other customers' portfolio data. All findings protected under privilege. Remediation completed in 8 days.
Cross-tenant data access identified
Tested a RAG system serving enterprise documentation. Found an embedding poisoning vector that allowed cross-tenant data access. DPDPA breach notification assessment provided alongside the technical fix.
Model inversion attack identified
Adversarial assessment of a diagnostic AI. Identified a model inversion attack that could reconstruct patient data from API responses. HIPAA and DPDPA compliance gap analysis delivered.
Excessive agency exploit found pre-funding
Full security audit of an autonomous coding agent. Discovered a tool-calling chain that enabled arbitrary file system access — exactly the Excessive Agency pattern OWASP flagged as the top-moving 2026 risk. Privilege-protected findings enabled the company to raise its funding round with a clean security posture.
Tools & methodologies
Our testing arsenal
Automated and manual tooling built specifically for transformer-based systems, not traditional pentesting frameworks adapted after the fact.
Sectors we protect
Industries deploying AI at scale
Why SIRI
Attorney-client privilege meets technical AI adversarial testing
Unlike standalone security firms, our findings are protected by legal privilege — critical when AI vulnerabilities could trigger regulatory scrutiny.
LLM-native methodology
Testing frameworks built specifically for transformer-based models, RAG architectures, and tool-calling agents — not adapted from traditional pentesting, and updated to reflect the 2026 shift toward agentic risk.
Privilege-protected findings
All security findings delivered under attorney-client privilege, preventing forced disclosure in regulatory investigations or litigation — an advantage no standalone technical consultancy can offer.
Rapid turnaround
Preliminary AI risk assessment in 72 hours. Full red team report in 10 business days. Remediation roadmap included with every engagement.
Regulatory-ready reports
Deliverables mapped to OWASP LLM Top 10 2026, the Agentic Top 10, NIST AI RMF, EU AI Act, and Indian regulatory frameworks. Board-presentable.
Frequently asked
AI & LLM security testing, answered directly
What types of AI systems do you test?
We test LLM-powered chatbots, RAG pipelines, agentic systems, code generation tools, AI decision-support systems, and any application built on foundation models including GPT, Claude, Gemini, Llama, and Mistral. Agentic systems — those with tool-calling, persistent memory, or multi-step autonomy — are assessed against both the OWASP LLM Top 10 2026 and the OWASP Agentic (ASI) Top 10, since the two frameworks cover different layers of the same deployment.
What changed in the OWASP LLM Top 10 2026, and why does it matter for testing scope?
The 2026 edition, published 4 August 2026, is the first built on real-world incident data — 7,714 incidents were gathered, with 6,639 carrying enough detail to be classified and weighted at 25% of the final ranking alongside a 75%-weighted practitioner vote. Prompt Injection and Sensitive Information Disclosure held the top two positions, but Excessive Agency jumped from sixth to third place, the largest move on the list. That shift reflects where real damage is now occurring: agentic systems that call tools, hold API access, or take autonomous actions. Our testing scope has been updated to weight tool-permission audits and human-authorization checkpoints as heavily as prompt injection resistance, rather than treating agentic risk as a secondary concern.
How is AI security testing different from traditional penetration testing?
Traditional penetration testing targets network and application layers. AI security testing targets the model itself and its surrounding architecture — prompt injection, training data leakage, output manipulation, RAG pipeline poisoning, and tool-calling exploits require methodologies built specifically for transformer-based systems, not traditional pentesting techniques adapted after the fact.
Are findings protected by attorney-client privilege?
Yes. Because SIRI is a law firm, security findings from an AI adversarial testing engagement are generally protected under attorney-client privilege, meaning they cannot be compelled in regulatory investigations or litigation in the way a standalone technical consultant's report typically can be. This matters specifically for AI findings, since a vulnerability like PII leakage or cross-tenant data exposure can itself constitute evidence relevant to a DPDPA or sector-regulator inquiry.
How long does an engagement take?
Preliminary risk assessment in 72 hours. Full adversarial red team report in 10 business days. SIRI Shield subscribers receive quarterly testing on a continuous basis.
Do you test third-party AI vendors we use?
Yes. Our AI vendor due diligence service assesses model governance, data processing terms, liability allocation, and the security posture of AI vendors before you sign or renew contracts.
What regulations apply to AI systems in India?
DPDPA 2023 applies to personal data processed by AI. MeitY has published India AI Governance Guidelines. RBI and SEBI have sector-specific AI/ML directives, including RBI's 2026 draft Guidance on Regulatory Principles for Model Risk Management. The EU AI Act applies if you serve European users. We map every finding to applicable frameworks.
Ready to secure your AI?
Book your free AI security assessment.
30-minute consultation. No commitment. Privilege-protected from the first conversation.
Related services
Other ways SIRI Law LLP secures AI-deploying organisations
Cybersecurity testing services
Full portfolio — application, cloud, network, and IoT security testing.
AI & emerging technology law
EU AI Act compliance, LLM vendor contracts, and algorithmic liability.
Data privacy & cybersecurity law
DPDPA compliance and breach response for AI systems processing personal data.
Visit or contact us
SIRI Law LLP — Hyderabad, India
| Registered office | HITEC City, Madhapur, Hyderabad, Telangana 500081, India |
| Telephone | +91 79819 12046 |
| info@sirilawllp.com | |
| Other offices | New Delhi, India · Austin, Texas, USA · Online worldwide |
| Hours | Mon–Sat, 9:30 AM – 7:00 PM IST · Emergency line 24/7 |