Secure AI before AI becomes your attack surface.
LLM security assessment, AI red teaming, prompt-injection testing and model risk assessment for CISOs and product owners shipping copilots, RAG systems and autonomous agents in production. We find what your penetration test cannot.
Your last penetration test did not test the model. Or the prompt. Or what the agent is allowed to do.
Conventional application security assumes code behaves deterministically and that input validation stops untrusted data from becoming instructions. Language models break both assumptions. The model is the interpreter, every document it reads is potential code, and the “application” now includes prompts, retrieval indexes, tool permissions and third-party model APIs.
A VAPT report with zero findings tells you nothing about whether a crafted email can make your support agent refund an account, or whether your HR copilot will reveal one employee’s salary to another. That is the gap AI security testing closes.
What we testSound familiar?
- A copilot is live over internal documents and nobody has tried to make it leak them.
- An agent has API keys to your CRM or ticketing system and its actions are not individually logged.
- Your LLM application reads customer emails, uploaded PDFs or web pages — untrusted content, straight into the prompt.
- Security signed off on the vendor’s SOC report, not on how your team actually wired the model in.
- Guardrails are a system prompt that says “do not”. Nobody has measured how often that holds.
- The regulator, auditor or board has asked for evidence of AI risk testing and there is none to show.
- Anyone can retrieve any chunk from the vector store, regardless of what they may read in the source system.
Assess the model as deployed — not the model on the vendor’s benchmark.
We test the language model inside your system prompt, your retrieval context, your tools and your output handling, because that is the configuration an attacker meets. The result is a severity-rated finding list with reproduction steps, not a generic risk narrative.
- Jailbreak and guardrail-bypass testing against your system prompt and content policy, with measured bypass rates
- Sensitive-data leakage: system prompt extraction, retrieval-context disclosure, cross-user and cross-tenant exposure
- Memorisation and training-data disclosure checks for fine-tuned or custom models
- Insecure output handling: model output reaching SQL, shell, HTML, email or downstream APIs without sanitisation
- Denial-of-wallet and resource abuse: unbounded context, recursive tool loops, cost amplification
Adversaries with a goal, not a checklist.
Automated scanners find known patterns. Red teaming answers a harder question: can a motivated attacker make your AI system do something your business would not accept? We define objectives with you, then attempt them the way an attacker would — across model, application and integrations together.
Objective-driven scenarios
Exfiltrate a customer record. Trigger an unauthorised refund. Get the clinical copilot to state a dosage it should refuse. Each objective is agreed, scoped and safe to run.
Multi-layer attack chains
Indirect injection through a document, escalated via a tool call, completed through an insecure output path. Findings show the full chain, not isolated defects.
Harm, bias and policy testing
Structured probes for discriminatory, unsafe or regulatorily unacceptable outputs relevant to your sector — lending, insurance, healthcare, public services.
Automated plus manual
Attack libraries and fuzzing establish baseline rates; specialists then craft the context-specific attacks that automation misses.
Evidence you can act on
Every finding carries severity, business impact, exact reproduction steps and a specific fix at the prompt, retrieval, permission or code layer.
Re-test and regression suite
Fixes are verified, and the attack set is handed over as a regression suite so the next prompt change does not silently reopen the hole.
If your model reads untrusted content, it can be given instructions by whoever wrote it.
Prompt injection is the defining vulnerability of LLM applications. Any system that summarises emails, answers over uploaded documents, browses the web or consumes tool output is exposed — and the payload does not need to come from the user. It can sit in an invoice, a résumé, a web page or a calendar invite.
- Direct injection: user-supplied attempts to override instructions, policy and role
- Indirect injection via documents, email bodies and attachments, web content, RAG chunks and API or tool responses
- Agent-specific tests: injected instructions that trigger tool calls, data exfiltration or privilege escalation
- Hidden-text, encoding and multilingual payloads, including content invisible to the human reviewer
- Defence validation: input isolation, privilege separation, output filtering and human confirmation points
Is this model fit for this decision?
Before a model influences a credit decision, a claim, a diagnosis or a citizen service, someone has to show it is accurate enough, stable enough and fair enough for that purpose — and that its failure modes are understood. Our assessment produces that evidence in a form risk committees and regulators recognise.
- Fitness-for-purpose evaluation on your data and your task, not vendor benchmarks
- Robustness under distribution shift, adversarial input and degraded context
- Bias and fairness analysis across protected and business-relevant groups
- Failure-mode catalogue: hallucination patterns, refusal gaps, over-confidence, silent errors
- Model inventory, risk tiering and documentation aligned to your model risk management framework
Secure everything wrapped around the model.
Agents, retrieval pipelines, model gateways and vendor APIs introduce attack paths that exist nowhere in a conventional application. We review architecture and configuration, then test each layer.
Agent tool permissions
Least-privilege review of every tool an agent can call, credential scoping, action logging, and confirmation gates on irreversible operations.
RAG & retrieval security
Index poisoning, document-level access control enforcement at retrieval time, cross-tenant leakage and embedding-store exposure.
Model access controls
Gateway authentication, per-application keys, rate limits, prompt and completion logging, and separation between development and production models.
API & integration security
Authentication, authorisation and input handling on the APIs the model calls and the APIs that call the model — tested with conventional and AI-aware methods.
Data leakage & logging
What leaves the boundary in prompts, telemetry and vendor logs; what is retained; and whether it matches your DPDP and residency commitments.
Third-party AI & vendor model risk
Due diligence on hosted models, AI SaaS and plug-ins: data handling terms, retention, sub-processors, model change control and exit options.
Quick test of your own estate: can anyone name every tool your agents can call, every credential behind them, and who is allowed to retrieve which chunk from the vector store? If not, start with the map rather than the test.
Request an AI attack surface mapA pen test with AI in scope versus AI security done properly.
Without a structured programme
- The model is a black box the tester talks to for an afternoon
- Injection is tested from the chat box only; documents and tool outputs are ignored
- Agent permissions are never enumerated, so over-privilege goes unnoticed
- Retrieval access control is assumed from the source system, not verified
- Vendor assurances stand in for testing your own integration
- Findings are narrative; nothing is reproducible or re-testable
With Avisha Global
- Model tested inside your prompt, context, tools and output handling
- Direct and indirect injection across every untrusted input channel
- Every agent tool and credential reviewed against least privilege
- Retrieval-time authorisation and index poisoning tested explicitly
- Vendor and hosted-model risk assessed alongside your own code
- Reproducible findings, verified fixes and a regression suite you keep
Evidence your CISO, auditor and board can use.
AI attack surface map
Every model, prompt, retrieval source, tool, credential and third-party AI dependency in scope, with trust boundaries drawn.
Severity-rated findings report
Each vulnerability with business impact, reproduction steps, evidence and a specific remediation at the right layer.
Prompt injection & jailbreak results
Measured success rates per attack class and input channel, so you know how robust the system actually is, not just whether one payload worked.
Model risk assessment file
Fitness, robustness, bias and failure-mode evidence, risk tier and documentation aligned to your model risk framework.
Regression attack suite
The test cases we used, packaged so your team can re-run them after every prompt, model or index change.
Executive & regulator summary
A short, non-technical statement of what was tested, what was found, what was fixed and what residual risk remains.
Scoped, safe to run in production-like environments, and finished with verified fixes.
A focused assessment of one AI application typically takes 3–5 weeks. Broader programmes covering several systems are phased.
Week 1 · Scope & threat model
Architecture walkthrough, data classification, agent tool inventory, attack objectives agreed with security and product owners.
Week 1–2 · Automated baseline
Attack libraries run across every input channel to establish jailbreak, injection and leakage rates before manual work begins.
Week 2–3 · Manual red team
Specialists pursue the agreed objectives across model, retrieval, tools and integrations, chaining weaknesses the way an attacker would.
Week 3–4 · Report & remediation
Findings walkthrough with engineering; fixes designed at the prompt, retrieval, permission and code layers with your team.
Week 4–5 · Re-test & hand-over
Fixes verified, regression suite delivered, executive summary issued, and monitoring recommendations passed to managed operations.
We build AI systems and we break them. That combination matters.
AI engineers and security testers on one team
Our testers know how agents, RAG pipelines and model gateways are actually built, because our engineers build them. Findings come with fixes that work in practice.
AI security plus conventional security
The same team covers VAPT, cloud, application and API testing, so AI findings are chained with the traditional weaknesses attackers actually use to reach them.
Built for the Indian regulatory context
Reports are written to satisfy DPDP obligations, sector regulator expectations and internal audit — with the governance hand-off already mapped.
Governance-ready output
Findings feed directly into the AI governance, model inventory and vendor-risk processes on our Digital Trust practice, rather than sitting in a PDF.
Safe to run
Objectives, environments and data handling are agreed in writing before testing starts. No production data is exfiltrated, no irreversible action is executed.
Testing that continues
Regression suites and optional periodic re-testing under Managed AI Operations keep assurance current as prompts, models and indexes change.
Security finds the risk. Governance keeps it managed.
An assessment is a point in time. AI governance sets the policy, ownership, model inventory, vendor controls and review cadence that keep every AI system accountable after the report is filed. Our Digital Trust practice takes the findings from here and turns them into an operating framework aligned to DPDP and sector expectations.
Your copilot has been reading customer emails, uploaded PDFs and web content for months, and nobody has yet tried to make it act on instructions hidden inside them. If you want to brief your own engineers before you brief us, take the field guide.
Prompt-injection field guide for security teamsFrequently asked questions
Find out what your AI systems expose.
Bring one copilot, agent or LLM application. We will scope a fixed-price assessment and tell you what an attacker could do with it before one tries.