End-to-End AI Red Team Platform
Red Teaming Supercharged.
XBOW Bench (XBEN)
93.27%
Overall Pass Rate
CVE-Bench
92.5%
Overall Pass Rate
HackTheBox CTF
#1
Ranked among all SEA Teams
Not another AI scanner — causal planning, evidence-backed closure, and an auditable Mission Workspace. Purpose-built models keep authorized pentesting stable as public LLM guardrails keep rising.
Strategy approved — scanning with purpose-built offensive models.
Ask about coverage, evidence, or approve next strategy…
Evidence layers
Confirmed
7
Security Leads
9
Recon Intel
8
Findings
IDOR on /enterprise/confirm
Broken function-level auth on CMS config
Email enumeration via checkUserEmail
Stack trace leakage on confirm
Verbose gateway error codes
RSAC 2026 Perspective
“You are going to be red-teamed whether you pay for it or not, the only difference is, you know who gets the results delivered to them.”
Rob Joyce, U.S. Homeland Security Advisor and NSA Cyber leader, RSAC 2026
Proactive Offensive Security turns unknown exposure into prioritized action. Instead of waiting for a real breach to reveal weak controls, security teams can continuously validate exploit paths, measure detection readiness, and deliver remediation evidence to engineering and leadership first.
Pilot User and Evaluation Partners
What sets PAIStrike apart
Four foundations that turn AI pentesting from brittle tool-calling into a platform you can trust to run continuously.
Condition-Tree Planning
Encode vulnerability preconditions as an executable prefix tree bound to User Stories. Unmet conditions prune whole subtrees — explainable, debuggable, auditable.
Evidence, Not Noise
Lead → validation → Confirmed. Public information alone is never a confirmed finding. Noise stays out of the report that matters.
Mission Workspace
Discover → decide → test → prove fix in one workspace. Autonomous or Collaborative gates, multi-run diffs, and remediation verification.
Purpose-Built Offensive Models
Public model guardrails keep tightening — legitimate authorized work gets refused more often. Our proprietary models are aligned for authorized offense: low false refusals, reliable tool use, continuous delivery.
Three ways to run offense
Same evidence discipline and agent stack — different entry points for red teaming, asset discovery, and CTF.
Authorized Red Teaming
End-to-end missions with Mission Workspace: map surface, deepen exploit paths, and close findings through evidence gates.
Primary product path
Asset & Attack Surface Scan
Discover and enumerate exposures across infrastructure and applications — build a structured attack-surface map before deep exploitation.
Recon-first discovery
CTF Mode
Competition-style and training targets with the same agent loop — proven on HackTheBox with #1 SEA ranking.
HTB · #1 SEA · 32/37 flags
Evidence-driven validation
Three output layers keep signal clean — so teams act on what is proven, not what is merely possible.
01 · Confirmed
Exploitation evidence cleared validation gates. Report-ready, reproducible.
02 · Security Leads
Promising signals under investigation — prioritized for deeper validation, not counted as confirmed.
03 · Recon Intelligence
Attack-surface facts and context that inform planning without inflating vulnerability counts.
Guru Pro
Built for offense that must not stall.
As open models raise safety rails, fewer options remain usable for real authorized pentesting. PAIStrike runs on Guru Pro — a purpose-built MoE model aligned for authorized offense — so agents can reason, call tools, and finish multi-step paths without false refusals breaking the loop.
- Aligned for authorized red-team context
- Low false-refusal rate on legitimate exploit workflows
- Stable tool use across long agent loops
Model card
Total parameters
862B
Active parameters
35B
Context window
1M
Architecture
Sparse MoE causal LM
Modalities
Text
Provider
AutoTrust
Sparse MoE
862B total · 35B active · 1M context
Full lifecycle — not a one-shot scan. From discovery to remediation verification, with AI that plans, validates, and documents every step.
Discover & Decide
Map assets and User Stories from seeds or known targets. Discovery is decoupled from scanning — you choose what enters scope before any exploit path runs.
Condition-Tree Planning
Vulnerability preconditions are encoded as an executable condition prefix tree bound to observed User Stories. Unmet conditions prune whole subtrees — only feasible skills enter the queue.
Exploit & Evidence Gates
Agents attempt real exploitation with purpose-built models tuned for authorized offense. Findings advance Lead → validation → Confirmed only when evidence gates pass — public info alone is never enough.
Deliver, Diff & Re-test
Mission Workspace delivers evidence-backed reports, multi-run diffs, and remediation verification (Fixed / Still present / Inconclusive) so the loop closes with proof of fix.
Top-Tier Performance
From common web flaws to complex attack chains, consistently validated.
XBEN Benchmark
104 Official Scenarios
Evaluation Engine
Scenario execution and verdict pipeline
Performance by Attack Complexity
Level 1 — Common Web Vulnerabilities
95.56%
Level 2 — Multi-step Attack Chains
90.20%
Level 3 — Stateful Attacks
100%
Vulnerability Coverage
Full coverage
Tags
Pass Rate
IDOR
10
93.33%
Privilege Escalation
10
92.86%
Command Injection
10
90.91%
Blind SQLi
6
66.67%
JWT
6
66.67%
XXE
6
66.67%
Arbitrary File Upload
5
50.00%
Overall Pass Rate
Built for measurable outcomes. PAIStrike is benchmarked against official, multi-category security scenarios to validate real exploitability at scale. Strong pass rates demonstrate consistent agent reasoning, reliable execution quality, and repeatable security outcomes that teams can trust in production workflows.
Built by Scantist. Grounded in Academic Research. PAIStrike is part of Scantist’s security platform, combining product-grade engineering with years of cybersecurity research from leading Singapore university labs. This foundation enables practical, reproducible red teaming outcomes for modern organizations.
Scantist AI Security Solutions
PAIStrike / AppDenfender / AI Defender
A focused portfolio for offensive validation, application protection, and AI security hardening under one security organization.
Learn more on Scantist.comResearch Leadership
Scantist’s direction is informed by deep academic cybersecurity research in Singapore, including leadership from Professor Liu Yang.
In today's rapidly evolving digital landscape, effectively translating cutting-edge cybersecurity research into actionable, measurable enterprise security outcomes has become the critical bridge between academic innovation and industry practice.
Professor Liu Yang
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Run proactive red teaming with causal planning, evidence gates, proprietary models, and remediation proof — in one workspace.