ThreatVault — Platform Walkthrough

1. Ingest

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2. Profile

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3. Fingerprint (future)

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4. Map (future)

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5. Validate (future)

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6. Remediate (future)

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● Live Feature

Threat Intelligence Ingestion

ThreatVault automatically collects and processes intelligence from multiple global sources — identifying active threat actors and their known indicators.

How ThreatVault Processes AI Threat Intelligence

① Collect

Raw AI threat data gathered from adversarial ML research, incident reports and security advisories

MITRE ATLAS adversarial technique matrix
AI Incident Database reports
Model theft & extraction incidents
Adversarial ML research papers & PoCs

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② Enrich

Each AI threat enriched with attack context and model targeting metadata

Attack vector — query API, training pipeline, model file
Model architecture targeted (LLM, CNN, GNN)
Known evasion success rates per framework
Affected runtimes (PyTorch, TensorFlow, ONNX)

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③ Correlate

Attack patterns linked through shared adversarial objectives and tooling

Campaign clusters by target model type
Shared perturbation families across actors
Infrastructure reuse across model theft ops
TTP similarity scores across AI-targeting groups

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④ AI Attribution

Graph model maps unattributed adversarial probes to known actor fingerprints

Query volume patterns linked to NeuralStrike
Perturbation signature similarity scores
Predicted extraction attempt timelines
Cross-campaign infrastructure overlap flags
New attack signatures added to actor profile

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⑤ Profile Ready

Complete adversary profile with AI-specific attack catalogue generated

Full NeuralStrike adversary dossier
MITRE ATLAS technique mapping
Known model targets & extraction methods
Sector focus & campaign history
Recommended AI-specific detection rules

Intelligence Sources Processed

Source	Type	Records	Actor Mentions
MITRE ATLAS v2.0	Framework	9,847	14
AI Incident Database 2024	Incident	312	7
Adversarial ML Research (arXiv)	Research	2,104	3
MLSecOps Community Feed	OSINT	891	5
NIST AI RMF Advisory	Advisory	28	2

Known Attack Signatures — NeuralStrike

Signature	Type	Severity	Framework
pgd-l2-eps0.3-iter40	Perturbation	CRITICAL	PyTorch
knockoff-nets-resnet50	Extraction	HIGH	Any
shadow-model-query-flood	Extraction	HIGH	TensorFlow
prompt-injection-v4-llm	Injection	MEDIUM	LLM APIs
data-poison-label-flip	Poisoning	HIGH	Any

AI Prediction

12 additional inference API endpoints identified as likely NeuralStrike reconnaissance targets based on query pattern similarity and model architecture exposure. Confidence: 71–89%.

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4. Map (future)

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5. Validate (future)

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6. Remediate (future)

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● Live Feature

Adversary Dossier

AI automatically generates a structured profile of the threat actor — mapping their tactics, tools, targets, and predicted infrastructure.

NeuralStrike — Adversary Dossier

Actor

NeuralStrike

Category

State-Sponsored / Industrial Espionage

Target Sectors

AI/ML Platforms, Autonomous Systems, Healthcare AI, FinTech ML

Primary Goal

Model intellectual property theft, adversarial evasion for downstream attacks

Active Since

Early 2023

Confidence

High — 8 confirmed model extraction incidents

Tactics, Techniques & Tools

ATLAS TECHNIQUES

AML.T0006 Model Extraction AML.T0043 Adversarial Evasion AML.T0019 Infer Training Data AML.T0051 LLM Prompt Injection

TOOLS USED

Knockoff Nets ART (Adversarial Robustness Toolbox) PromptInject v4

ACTIVE CAMPAIGNS

Operation ModelHeist (2024) CognitiveBlind (2024) ShadowQuery (2023)

🔲 Future Phase

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6. Remediate (future)

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◎ Roadmap

Target Asset Discovery

ThreatVault automatically scans the target organization's digital footprint — identifying exposed systems, services, and potential weaknesses.

Acme Corp — AI Assets Discovered

Asset	Type	Exposure	Risk
Fraud Detection Model API	Inference API	Public endpoint	HIGH
Document OCR Pipeline	ML Pipeline	Internal	MEDIUM
Recommendation Engine	Inference API	Public endpoint	HIGH
MLflow Model Registry	Model Store	Internal (open S3)	CRITICAL
LLM Chat Assistant	LLM API	Public endpoint	CRITICAL

Vulnerabilities Identified

Weakness	Asset Affected	Severity
No query rate limiting on inference API	Fraud Detection API	CRITICAL
LLM exposes system prompt via injection	LLM Chat Assistant	CRITICAL
Model file in unauthenticated S3 bucket	MLflow Registry	HIGH
Recommendation API returns raw logits (extraction-ready)	Recommendation API	HIGH

🔲 Future Phase

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6. Remediate (future)

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◎ Roadmap

Threat-to-Target Mapping

ThreatVault matches the threat actor's known capabilities directly against the target organization's exposed assets — producing a personalized risk score.

Threat-to-Asset Match — NeuralStrike vs Acme Corp

Actor Capability	Matching Asset	Match	Risk
Model Extraction (AML.T0006)	Fraud Detection API — no rate limit	DIRECT	CRITICAL
Prompt Injection (AML.T0051)	LLM Chat Assistant	DIRECT	CRITICAL
Model File Theft	Unauthenticated S3 model bucket	DIRECT	HIGH
Logit-Based Clone (AML.T0043)	Recommendation API returns raw logits	PARTIAL	HIGH

Overall Risk Score — CRITICAL

NeuralStrike's model extraction toolkit directly matches 3 exploitable weaknesses in Acme Corp's AI stack. The unprotected fraud detection API could be fully cloned via Operation ModelHeist techniques in under 48 hours.

Risk Breakdown

Direct Model Download via Open S399%

Fraud Model Extraction via Query Flood96%

LLM Prompt Injection / System Prompt Leak91%

Recommendation Model Clone via Logits67%

🔲 Future Phase

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◎ Roadmap

Safe Threat Validation

Firecracker spins up an isolated replica of the target environment and safely tests whether the identified threats can be exploited — without touching live systems.

Firecracker — AI Robustness Validation

Test	Target	Technique	Result
Knockoff Nets Model Clone	Fraud Detection API	AML.T0006	VULNERABLE
Prompt Injection — System Prompt Leak	LLM Chat Assistant	AML.T0051	VULNERABLE
S3 Unauthenticated Model Download	MLflow Registry	File Access	VULNERABLE
PGD Adversarial Evasion — Fraud Bypass	Fraud Detection API	AML.T0043	PROTECTED

Validation Summary

3 of 4 attack paths confirmed exploitable. NeuralStrike could clone the fraud detection model in ~48 hours using public query-based extraction tools. The unauthenticated S3 bucket exposes the model file directly — no queries needed. Immediate action required.

Isolated Test Environment

SAFE Tests run in fully isolated Firecracker microVMs

SAFE No live production AI models were queried

SAFE Environment destroyed after testing

INFO Test duration: 9 min 43 sec

INFO Environment snapshot: acme-ai-stack-v2.1

🔲 Future Phase

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2. Profile

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3. Fingerprint (future)

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4. Map (future)

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5. Validate (future)

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6. Remediate (future)

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◎ Roadmap

Remediation & Response

ThreatVault generates a complete remediation package — from technical fixes for your security team to a plain-English summary for your leadership.

Technical Fixes

①
Lock S3 model bucket — restrict access to authorised IAM roles only, enable bucket policy and versioning
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Rate-limit fraud detection API — max 500 queries/min per client; require API key authentication
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Deploy LLM input/output guardrails — block system prompt extraction patterns and PII leakage
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Switch recommendation API to return class labels only — strip raw logit values from responses

Detection Rules Generated

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SIEM rule: Alert on inference API — >200 queries/min from single IP (extraction probe pattern)
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SIEM rule: Alert on S3 GetObject calls to model-registry bucket from outside VPC
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LLM guardrail: Block prompt patterns matching known injection templates (PromptInject v4 signatures)
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Model watermark: Embed steganographic fingerprint — detect if cloned model is deployed externally

Executive Summary

Board Summary

A threat group targeting AI companies can steal your fraud detection model today — it's sitting in a public storage bucket. Once stolen, adversaries can craft transactions that bypass your model undetected. Four specific fixes close this gap this week.

PDF Report Ready STIX Export Ready Detection Rules Ready