Sectum AI vs Microsoft PyRIT

TL;DR. Microsoft’s PyRIT (Python Risk Identification Tool) is the “Metasploit for LLMs” — a powerful open-source framework with 6 attack strategies, 70+ prompt converters, 53+ datasets, multimodal support, and a memory system. It’s a toolkit security professionals build on top of. Sectum AI is the opinionated product for one specific category — multi-tenant verification with tamper-evident, control-mapped evidence. PyRIT gives you the building blocks; Sectum AI gives you the finished category-specific outcome. PyRIT could be used to author multi-tenant probes, but you’d build the substrate, the manifest, the evidence chain, and the audit pack yourself.

The two products

Microsoft PyRIT (Azure/PyRIT)

Category: open-source AI red-team automation framework. Built from Microsoft’s experience red-teaming production AI systems (Bing Chat, Copilot).

License: MIT. 3.6k GitHub stars (April 2026). Latest v0.11.0 (February 2026). Python 3.10-3.13.

Capability surface (PyRIT homepage, PyRIT arXiv paper, Microsoft TechCommunity overview):

Orchestrators that manage multi-turn AI conversations.
70+ converters to transform prompts and bypass filters: Base64, ROT13, Leetspeak, Unicode confusables, LLM-powered rephrasing, translation, multimodal injection.
Scorers to evaluate whether attacks succeeded.
Memory system that tracks every probe attempt.
Multimodal: text, image, audio, video.
53+ datasets — AIRT, HarmBench, AdvBench, XSTest — covering content harms, jailbreaks, data exfiltration, social bias.
6 attack strategies — PromptSendingAttack (single-turn), CrescendoAttack (gradual escalation), TreeOfAttacksWithPruning (TAP), multi-turn dialogue attacks.

Pricing: free / OSS. Microsoft also offers a managed AI Red Teaming Agent in Azure AI Foundry — sits on top of PyRIT for the customer who wants a managed experience.

Buyer: security professionals automating AI red-team work, Azure AI Foundry users, research labs and AI safety teams.

Sectum AI (sectum.ai)

Category: multi-tenant AI verification — a focused product, not a framework.

License: Apache 2.0 OSS core (substrate, attack catalog, adapters, evidence chain, sectum-ai verify). Sectum Cloud commercial. The evidence layer in the OSS produces the same artifacts the hosted product does — by design.

Shape: a focused product, not a framework. The marker substrate is provided. The 11 probe classes are pre-built. The evidence chain is wired (RFC 3161 TSA + Sigstore Rekor + in-toto envelope). The audit-pack PDF is rendered. The sectum-ai verify command is OSS. You don’t build any of it; you point it at a stack and get an attestation.

Framework vs. focused product

The central distinction:

	PyRIT	Sectum AI
Shape	A framework / toolkit (Metasploit for LLMs)	A focused product for a specific category
What you assemble	Orchestrators + converters + scorers + memory + datasets — to build your own red-team campaigns	Nothing — you run the CLI; the substrate, probes, evidence chain, and audit pack are pre-built
Multi-tenant focus	Not specifically (it’s general-purpose)	The category
Evidence layer	Memory system for the practitioner; you build the report	Tamper-evident audit pack: RFC 3161 + Rekor + in-toto + PDF + JSON, independently verifiable
For	Security engineers doing custom red-team	CISOs, DPOs, audit firms
Output cadence	Per-experiment	Per-engagement (e.g., GDPR Art. 17 erasure attestation)
Time to first attestation	Days-to-weeks (you build it)	Minutes (you run it)

A useful analogy: PyRIT is to AI red-team what Metasploit is to network pentesting — a framework with primitives, where the practitioner does the creative work and assembles the campaign. Sectum AI is to multi-tenant verification what Nessus is to vulnerability scanning — a focused product with a known outcome shape, run repeatedly against changing targets.

Both are valuable. Neither replaces the other.

Where PyRIT is the right tool

You have a security team that writes attack code and wants flexibility.
You’re researching a novel attack pattern (e.g., a new converter, a new orchestrator strategy) and need a framework to prototype on.
You’re operating in the Azure AI Foundry ecosystem and want first-party Microsoft tooling.
You need multimodal red-team (text + image + audio + video) — PyRIT is one of the few frameworks built for this from day one.
You want maximum control over how the red-team is composed (converters, scorers, datasets, attack strategies — all swappable).
You’re a research lab publishing on AI safety where reproducibility of attack methodology matters.

Where Sectum AI is the right tool

You need multi-tenant infrastructure verification as a specific, repeatable outcome — not a custom-built red-team campaign.
You need auditor-acceptable, tamper-evident evidence — sectum-ai verify validates the cryptographic chain end-to-end without Sectum AI in the room.
You’re facing a GDPR Article 17 erasure obligation for a churned tenant and need post-deletion AI-surface attestation for a DPO or regulator.
You’re preparing for SOC 2 / ISO 27001 / HIPAA on a multi-tenant AI product and need per-finding control mappings (OWASP / ATLAS / NIST) into your audit evidence.
You don’t have a dedicated AI red-team and want a finished product that produces a known-good evidence pack on each run.

Could you use PyRIT to build what Sectum AI does?

Technically, yes — and that’s a useful question to think through.

PyRIT’s primitives (orchestrators, converters, scorers, memory) are general enough that a skilled team could author multi-tenant probes on top of them. To replicate the coverage Sectum AI ships you’d need to build:

A synthetic-tenant substrate that generates realistic per-tenant corpora with shared organic entities (reproducing the Retrieval Pivot conditions).
A canary-marker system with three marker types (HARD / ENTITY / SECRET), hashed manifest, and per-tenant ownership tracking.
A layered detection pipeline (exact → semantic → calibrated judge) with manifest-grounded zero-FP guarantees on confirmed findings.
11 probe classes covering tenant-boundary fetch, entity-bleed RAG, RAG poisoning, semantic cache, KV-cache timing, embedding inversion, agent-tool hijack, memory contamination, LoRA cross-tenant, IKEA extraction, and GDPR erasure verification — each with per-surface adapter coverage.
Live adapters for vector DBs (Pinecone, pgvector, Weaviate, Chroma), caches (Redis), observability backends (Langfuse, LangSmith, Phoenix), agents (HTTP / generic), and MCP (stdio).
An evidence chain: canonicalization, SHA-256, RFC 3161 TSA, Sigstore Rekor, in-toto envelope.
Per-finding control mappings (OWASP LLM08:2025 / ATLAS / NIST AI RMF) on every finding.
An audit-pack PDF renderer that an auditor or DPO accepts.
A verify command that’s installable on a third party’s machine.

That’s roughly six months of engineering for the build, plus ongoing per-class research and per-backend adapter maintenance. Sectum AI exists so you don’t.

Honest positioning

PyRIT is one of the best general-purpose frameworks for AI red-team in 2026 — Microsoft-backed, well-documented, broad capability surface, multimodal-from-day-one. The right tool when you need flexibility and have a security team to wield it.

Sectum AI is a focused, evidence-first verifier for multi-tenant AI isolation — pre-built, repeatable, auditor-grade. The right tool when you specifically need to prove the tenant boundary holds and produce attestation an auditor or DPO accepts.

A serious AI security program at a multi-tenant SaaS will likely use both: PyRIT for custom red-team work, Sectum AI for multi-tenant verification.

Pricing

PyRIT — free, MIT. Available on GitHub.
Azure AI Foundry AI Red Teaming Agent — Azure-priced; sits on top of PyRIT for the managed experience.
Open Sectum (OSS) — free, Apache 2.0.
Sectum Cloud — see pricing.

References

PyRIT — Azure/PyRIT on GitHub, PyRIT homepage, arXiv paper, 2026 review (AppSec Santa), Securing AI Agents Before They Ship (Microsoft TechCommunity), Microsoft Foundry AI Red Teaming Agent.
Sectum AI — GitHub, docs, attack catalog, evidence chain, sample evidence packs.