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Aquila I AI Red Teaming
Your chatbot passed every functional test. Nobody has yet tried to talk it into misbehaving — Aquila I AI Red Teaming attacks your own LLM apps, RAG pipelines, agents and MCP servers before and after release, then scores each system from 0 to 100 and maps every finding to OWASP, NIST AI RMF and MITRE ATLAS.
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How it’s rated
Full scoreboard ↓Quick answer
The Aquila I platform family
This page covers Aquila I AI Red Teaming — adversarial testing before and after release. The rest of the Aquila I platform:
Quick facts
30-second orientation- Product
- Automated adversarial testing of LLM apps, RAG pipelines, autonomous agents, tool integrations and MCP servers
- Maker
- Aquila I Solutions Pvt. Ltd., Thane, Maharashtra; founded 2020; no outside funding reported (Tracxn)
- Status
- Aquila I’s newest product, datasheet dated September 2026; no analyst placement, no named customers
- Delivery
- The call to action is “Request an assessment”; whether you run it or Aquila I runs it is not stated
- Modes
- Scan: hundreds of single-turn probes for breadth; Exploit: stateful, multi-turn adaptive attack chains
- Score
- A Trust Score from 0 to 100 per AI system, built from indicators such as hardcoded keys
- Frameworks
- Findings mapped to OWASP LLM Top 10, OWASP Agentic AI Top 10, NIST AI RMF and MITRE ATLAS
- Price
- Quote-only; no published price, no INR list and no published unit per target, scan or assessment
- India
- Built in India, but where tests run and where transcripts and reports are kept is not published
- In India via
- TechBag — AI target inventory, engagement scoping, quote in INR with GST, a first test cycle
Understand AI red teaming before you buy it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is AI red teaming?
Attacking your own AI systems the way an adversary would, before and after release, to prove what breaks.
A manual jailbreak checklist vs Aquila I AI Red Teaming — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | A manual jailbreak checklist | Aquila I AI Red Teaming |
|---|---|---|
| Who writes the attacks | A tester with a jailbreak list | An attacker LLM fitted to each target |
| Depth of a test | Single prompts, judged by eye | Multi-turn Exploit chains with transcripts |
| Agents and tools | Outside a web penetration test | Goal hijack, tool abuse and MCP attacks tested |
| The result | A PDF of findings, once a year | A 0–100 Trust Score tracked run to run |
| Audit evidence | Findings tagged by hand | Mapped to OWASP, NIST AI RMF and MITRE ATLAS |
| What it is NOT | — | Runtime protection, a CERT-In audit, or a published price |
The cheapest test is one staging system: run Scan mode for a baseline, Exploit the worst finding, fix it, and re-test the score.
The four pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Profiling
Each AI system is profiled for its purpose, tools, boundaries and refusal patterns, so a healthcare RAG is probed for patient data and a finance agent for payments.
Generation
An attacker LLM writes adversarial probes fitted to the system’s context, data types and industry, alongside the research-grade static attack sets Aquila I includes.
Execution
Scan mode fires hundreds of single-turn probes across every category for a baseline; Exploit mode holds stateful, multi-turn conversations that adapt to prove impact.
Grading
Findings pass multi-layer grading, roll up into a 0–100 Trust Score mapped to four frameworks, and land in executive and technical reports with full transcripts.
Profile the target, let an attacker LLM write the probes, Scan wide and Exploit deep — then grade, score and map every finding.
Nine capabilities. Profile, attack, report.
Aquila I AI Red Teaming attacks the AI you build before someone else does — and turns each finding into evidence.
Finds the AI to test
Aquila I’s site describes discovery across source code, containers, network calls to AI APIs and Terraform or CloudFormation templates.
Attacks fitted to the target
Before any probe fires, the target’s purpose, tools, data types and industry shape the attacks, rather than one generic checklist.
Tool-using agents tested
Probes for goal hijacking, memory poisoning, privilege escalation and unintended lateral actions by agents that call real tools.
Jailbreaks and prompt leaks
Role-play, encoding and multi-turn crescendo jailbreaks, system-prompt extraction, and direct or indirect prompt injection attempts.
Retrieval pipelines probed
Retrieval poisoning, grounding erosion, citation manipulation and cross-tenant or cross-session leakage in RAG-backed applications.
Protocol-layer attacks
Tool-description poisoning, parameter injection and cross-server compromise are tested at the Model Context Protocol layer itself.
0–100 for each AI system
Built from indicators such as hardcoded API keys, shadow deployment, internet exposure, excessive permissions and missing audit logs.
Four frameworks, every run
Every finding is mapped deterministically to OWASP LLM Top 10, OWASP Agentic AI Top 10, NIST AI RMF and MITRE ATLAS.
Transcripts and playbooks
Executive and technical reports carry full exploit transcripts, a prioritised remediation playbook and a compliance evidence pack.
An AI that can take actions can be talked into the wrong ones. Aquila I AI Red Teaming tries first.
Here’s what genuinely sets it apart — and exactly where it stops.
Two modes: breadth first, then proof
Scan mode runs hundreds of fast single-turn probes across every category to set a baseline, and to re-test after each model update. Exploit mode then holds stateful, multi-turn conversations that adapt as the target answers, and keeps the full transcript as evidence of impact for engineers and the board.
Agents and MCP, not only chatbots
Aquila I aims its testing at what an AI can do, not only what it says: goal hijacking, memory poisoning and privilege escalation in tool-using agents, and tool-description poisoning, parameter injection and cross-server attacks on MCP servers, the protocol that now wires agents to business systems.
A score and a framework map for the auditor
Each system gets a Trust Score from 0 to 100 built from concrete indicators, and each finding maps to OWASP LLM Top 10, OWASP Agentic AI Top 10, NIST AI RMF and MITRE ATLAS. Aquila I says scores can drive policy, so a high-risk system is flagged or blocked before it reaches production.
Where it stops
It is not clear whether you run it or Aquila I runs it for you: the call to action is “Request an assessment”. No price, unit, hosting region or sample report is published, and no named customer is cited. It is not a CERT-In-empanelled audit, and it tests the AI you build, not staff use of AI tools.
The numbers behind the platform
What your Aquila I AI Red Teaming rollout looks like
List the AI you build
Inventory LLM apps, RAG pipelines, agents and MCP servers, the data each can reach, and which of them are already live.
Settle tool or service
Get Aquila I to state who runs the tests, what access it needs to code, containers or cloud, and where transcripts are kept.
Scan one staging system
Run Scan mode on one non-production target for a baseline Trust Score, and walk the framework mapping with your auditor.
Exploit, fix, re-test
Run Exploit mode on the riskiest findings, apply the remediation playbook, and re-test to confirm the score has moved.
Make it a release gate
Schedule re-tests after each model or prompt change, agree the score that blocks a release, and sign the quote in INR.
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Exploit mode talked our loan-servicing agent into calling a tool it should never touch. The transcript made the fix obvious.”
“Scan mode gave us a baseline within a day; we now re-run it after every model or prompt change before each release.”
“The OWASP and NIST AI RMF mapping went straight into our risk file, and the auditor read it without a translator.”
“Our MCP server exposed a tool to a second server during testing. Nobody on the team had thought to check that path.”
“Good findings, but it took two calls to learn whether we were buying software or an assessment run by their team.”
“The Trust Score helped us prioritise, though we had to ask where transcripts were stored before legal would sign.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and review-site grids score satisfaction, not fit for your estate. So here’s TechBag’s synthesis of the AI red-teaming market — tap any vendor to see why it sits where it does.
TechBag AI Red-Teaming Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Quote-only; the newest product, datasheet September 2026.
Where Tests Run × Attack Depth
The grid nobody publishes — how much control you get over where tests and transcripts live, India included, vs how deep the attacks go on agents, tools and multi-turn chains.
Hosting unstated; Scan and Exploit modes, agents and MCP named.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
Aquila I AI Red Teaming vs the AI red-teaming field
Against F5 AI Red Team, Palo Alto Prisma AIRS AI Red Teaming, Check Point AI Red Teaming, Promptfoo and Microsoft PyRIT — on delivery, attack method, agents and MCP, frameworks, scoring, price, India and exit.
| Dimension | Aquila I AI Red Teaming | F5 AI Red Team | Palo Alto Prisma AIRS AI Red Teaming | Check Point AI Red Teaming | Promptfoo | Microsoft PyRIT |
|---|---|---|---|---|---|---|
| What it is | Automated AI red team | Agent-swarm AI testing | Red teaming in AIRS | Formerly Lakera Red | Open-source red teaming | Microsoft OSS framework |
| Delivery and deployment | Unclear: tool or service | 3 clouds or OpenShift | SaaS via Strata | Hosted platform | Local, cloud or on-prem | Your own Python setup |
| Attack method | Attacker LLM, 2 modes | Attacker-agent swarms | Library, agent, custom | Objectives + strategies | Plugins, then grading | Crescendo, TAP and more |
| Attack coverage | 8 areas, no count | 10,000+ new a month | 50+ techniques | 23 default objectives | Plugin catalogue | Build your own |
| Agents and MCP | Agents and MCP named | Agents; MCP not stated | Agents; MCP not stated | Not described | Authorisation plugins | Build your own |
| Frameworks mapped | Four, deterministic | Not verified | OWASP, ATLAS, NIST | Own three categories | OWASP, NIST, EU AI Act | Not documented |
| Scoring and reports | 0–100 Trust Score | Format unpublished | 0–100 risk score | Severity per finding | Graded vulnerabilities | Scorers + memory |
| Finding to runtime fix | Context for AIS DR | Into AI Guardrails | Informs AIRS runtime | Mapped to Guard levels | Gate, then fix | No runtime layer |
| Pricing model | Quote; unit unknown | Quote; unit unpublished | NGFW credits | Enterprise quote | Free tier, then custom | Free, MIT licence |
| Published entry price | Not published | Not published | No public rate | No list price | $0 Community tier | $0 open source |
| Support | 24×7, by its account | Per F5 contract | Palo Alto portal | Enterprise contract | Community or SLA | Community only |
| India data location | Not published | Your OpenShift cluster | Not in India | Not published | Self-host anywhere | Runs where you run it |
| Lock-in and exit | Best inside Aquila I | Best inside F5 | Strata platform | Check Point AI stack | Open-source exit | MIT, no vendor tie |
| Best fit | Agent and MCP builders | F5 shops securing AI | Palo Alto AI estates | Check Point AI stacks | Developers in CI/CD | In-house red teams |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Aquila I AI Red Teaming if…
- ✓You ship LLM apps, RAG pipelines or MCP-connected agents and need them attacked before release and after each change
- ✓Auditors or a model-risk committee want findings mapped to OWASP, NIST AI RMF and MITRE ATLAS on every run
- ✓You use, or plan, Aquila I AIS DR and want test findings to add context to alerts from production
Compare alternatives if…
- ✓You want to run the tests yourself, free, today — Promptfoo’s Community tier and Microsoft PyRIT cost nothing to license
- ✓Tests and transcripts must stay on your own Indian servers — F5 runs as an OpenShift operator on your own cluster
- ✓Findings should become runtime blocks — F5, Palo Alto and Check Point pair testing with their own guardrails
Do not expect…
- ✓A clear answer from the website on whether this is software you run or an assessment Aquila I runs
- ✓Runtime protection: blocking live attacks is AIS DR’s job, or another guardrail’s
- ✓A published price, hosting region, sample report or named customer case study
TechBag has no AI security guide yet, so Aquila I AI Red Teaming sits outside the category guides. Browse all products to compare it with the rest of the catalogue. →
What does testing AI by hand cost you?
Drag the sliders (adversarial test cases your team runs by hand a year; engineer-hour cost). Estimates model the time spent writing, running and documenting each manual AI test case at an assumed 1.5 hours apiece, with 70% of it removed by automated attack generation, grading and reporting. Both figures are TechBag assumptions. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
Quote only. Aquila I publishes no price for AI Red Teaming, no INR list and no unit, so whether AI systems, scans, assessments or a subscription are counted is the first question. Its call to action is “Request an assessment”, and whether you run the tests or Aquila I runs them is not stated. The hosting region and a sample report are not published. AIS DR, the production half of Aquila I’s Security for AI, is quoted separately. TechBag lists your AI systems first, then gets the quote itemised in INR with GST.
Aquila I AI Red Teaming
Best for teams shipping LLM apps and agents
- Quoted; no published unit
- Scan and Exploit modes, 0–100 score
- Four frameworks mapped on every run
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ Aquila I AIS DR
Best when the AI is already live
- Detects misuse of AI in production
- Red-team findings add alert context
- Quoted as its own line
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your requirements and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
Is this software your team runs, or an assessment Aquila I runs for you? Who starts each test, and how many are included?
What access does discovery need — source code, containers, network traffic, cloud templates — and can it be read-only?
Where are probes run from, and where are exploit transcripts and reports stored? Get the region committed in writing.
How are targets counted for the quote — per AI system, endpoint, scan or assessment — and is there a yearly cap?
Can Exploit mode be kept to staging, and what stops a multi-turn attack triggering real tool actions in production?
Is a high Trust Score good or bad, which indicators move it, and is the method stable run to run? Ask for a sample.
Ask for a sample report showing one finding mapped to OWASP LLM, OWASP Agentic AI, NIST AI RMF and MITRE ATLAS.
Aquila I publishes no named customer case studies; can it arrange a reference call with a comparable Indian AI team?
Questions buyers ask
Ready to evaluate Aquila I AI Red Teaming?
List the LLM apps, RAG pipelines, agents and MCP servers you build first, or let a TechBag advisor settle tool-or-service, access and hosting in writing before a first test.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.