AI SHIELD

114 modules. 17 verticals. Zero trust. Full coverage.
Autonomous defence for AI agent fleets. 114 modules. Real-time threat detection.
114
Modules
17
Verticals
100%
ATLAS Coverage
Real-Time
Detection
Launch AI Shield GUI Read Documentation
AI AGENT HIJACKING · PROMPT INJECTION · MEMORY POISONING · TOOL ABUSE · CONTEXT MANIPULATION · GUARDRAIL BYPASS · SUPPLY CHAIN ATTACKS · ROGUE MCP SERVERS · MODEL INVERSION · DATA EXFILTRATION · AI AGENT HIJACKING · PROMPT INJECTION · MEMORY POISONING · TOOL ABUSE · CONTEXT MANIPULATION · GUARDRAIL BYPASS · SUPPLY CHAIN ATTACKS · ROGUE MCP SERVERS · MODEL INVERSION · DATA EXFILTRATION ·

AI Agents Are the New Attack Surface

AI agent fleets operate autonomously, chain tools, hold persistent memory, and trust external inputs by default. Every one of those properties is an attack vector. Traditional perimeter security has no concept of prompt injection, memory poisoning, or agent impersonation. AI Shield was built for this gap — 114 modules covering every layer from model inference to fleet orchestration.

LLM01 / ATLAS AML.T0051

Prompt Injection

Direct and indirect injection attacks that override agent instructions, hijack goal state, or exfiltrate context through manipulated tool outputs and poisoned data sources.

ATLAS AML.T0040

Memory & Context Poisoning

Adversarial writes to agent memory stores — vector databases, episodic buffers, working context windows — that persist across sessions and corrupt downstream reasoning at scale.

LLM07 / ATLAS AML.T0048

Tool Abuse & Excessive Agency

Agents granted access to file systems, APIs, and code execution can be weaponised through over-privilege. AI Shield enforces least-privilege tool policy at the agent runtime layer.

OWASP LLM02

Data Exfiltration via Output

Sensitive information leaked through model outputs, encoded in structured responses, or smuggled via tool call parameters. Detection patterns across 114 exfiltration signatures.

LLM03 / Supply Chain

Supply Chain Compromise

Malicious adapters, poisoned fine-tune checkpoints, and backdoored MCP servers masquerading as legitimate tool endpoints — all intercepted before they reach the agent runtime.

ATLAS AML.T0056

Rogue MCP Servers

Adversary-controlled Model Context Protocol servers that inject malicious instructions, exfiltrate agent state, or perform tool poisoning through the MCP stdio and SSE transport layers.

114
Defence Modules
17
Industry Verticals
100%
MITRE ATLAS Coverage
<50ms
Detection Latency

17 Industry Verticals

AI Shield deploys across 17 purpose-built verticals. Each vertical packages the modules most relevant to that industry's threat model, regulatory obligations, and AI deployment patterns — from financial services fraud prevention to space/NTN autonomous systems assurance.

V01
Core
ACTIVE
V02
Adversarial
ACTIVE
V03
Injection
ACTIVE
V04
Exfiltration
ACTIVE
V05
Model Security
ACTIVE
V06
Agent Runtime
ACTIVE
V07
Supply Chain
ACTIVE
V08
Compliance
ACTIVE
V09
Network
ACTIVE
V10
Cryptographic
ACTIVE
V11
Infrastructure
ACTIVE
V12
Social Engineering
ACTIVE
V13
Ransomware
ACTIVE
V14
API Security
ACTIVE
V15
Multimodal
ACTIVE
V16
Mobile
ACTIVE
V17
Space / NTN
ACTIVE

Purpose-Built Defence Modules

Every module in AI Shield is a standalone detection engine with its own signature library, behavioural heuristics, and MITRE ATLAS mapping. Modules compose into vertical deployments without interference — each one independently testable, independently deployable, independently auditable.

M99 — CORE
Prompt Injection Shield
Real-time interception of direct and indirect prompt injection across all agent input channels. Covers goal hijacking, instruction override, role manipulation, token smuggling, and context overflow patterns. OWASP LLM01 mapped. Sub-50ms detection on every inference call.
LLM01 ATLAS AML.T0051 Real-Time V01 Core
M104 — ADVERSARIAL
Adversarial Input Detector
Detects adversarial ML attacks against vision and text models — FGSM, PGD, CW, patch attacks, and semantic adversarial examples. Validates inputs before they reach model inference. Integrates with NIGHTFALL FORGE test findings to generate blocking rules automatically.
FGSM / PGD ATLAS AML.T0043 V02 Adversarial VLM Support
M108 — AGENT RUNTIME
Agent Runtime Monitor
Continuous behavioural monitoring of live AI agents. Detects anomalous tool call sequences, memory write patterns, inter-agent messaging abuse, and goal-state drift. Works across LangChain, AutoGen, CrewAI, and custom agent frameworks via the AI Shield instrumentation layer.
LLM06 Behavioural V06 Agent Runtime MITRE ATLAS
M300 — SPACE / NTN
NTN Shield
Purpose-built for Non-Terrestrial Network AI systems. Covers satellite-ground link injection, feed manipulation, orbital command spoofing, and firmware integrity verification. SPARTA framework mapped. Supports LEO, MEO, GEO, and HAPS deployments with latency-tolerant detection pipelines.
SPARTA NTN / 5G NR V17 Space 140 Tests

Full Fleet Visibility. One Interface.

Real-time threat telemetry across all 114 modules — live in the AI Shield Command GUI:

[AI SHIELD COMMAND] v3.0.0 — 114 modules active — 17 verticals

[FLEET STATUS] 108/109 modules HEALTHY ORCHESTRATOR LIVE
[THREAT FEED] Real-time · MITRE ATLAS mapped · OWASP LLM Top 10

[M99 CORE] CLEAN 0 injections detected (last 60s)
[M104 ADVERSARIAL] CLEAN FGSM/PGD patterns: 0 anomalies
[M108 AGENT RUNTIME] ALERT Anomalous tool chain: AGENT-007
[M110 SUPPLY CHAIN] CLEAN Adapter checksums verified
[M300 NTN SHIELD] CLEAN Satellite feed integrity: NOMINAL

[ATLAS COVERAGE] 100% — all tactics mapped
[DETECTION LATENCY] avg 23ms · p99 48ms
[BLOCKED LAST 24H] 47 threats blocked · 0 false positives confirmed

Designed for Autonomous Operation

AI Shield runs as a containerised microservice fleet. Each module is an independent FastAPI service behind a central orchestrator. Zero shared state between modules — a compromised module cannot contaminate the fleet. Designed to operate inside air-gapped environments, Kubernetes clusters, and CI/CD pipelines.

Containerised Fleet

Every module ships as a standalone Docker container. UBI9-certified images for enterprise deployments. Zero external runtime dependencies. Fully air-gap capable.

Real-Time Detection Pipeline

Sub-50ms detection latency at p99. Modules run in parallel — no serial bottlenecks. Threat signals routed to SIEM within 100ms of detection via Splunk HEC, CEF, or LEEF.

NIGHTFALL Integration

AI Shield and NIGHTFALL share a bidirectional threat feed. NIGHTFALL offensive findings automatically generate AI Shield blocking rules — closing the loop between testing and production defence.

Zero Trust Module Isolation

No module trusts another. Each module validates its own inputs, maintains its own signature database, and communicates only through the orchestrator API. Compromise radius is bounded to a single module.

Behavioural Baselines

AI Shield builds statistical baselines for every agent it monitors. Anomaly detection uses Mahalanobis distance against the baseline — not static rules. Adapts to fleet changes automatically.

API & SDK Integration

REST API with OpenAPI spec. Python SDK published to PyPI. Hooks for LangChain, AutoGen, CrewAI, and custom agent orchestrators. Middleware injection supported for transparent deployment.

Every Threat Mapped to Every Standard

100% Coverage

MITRE ATLAS

  • AML.T0051 — LLM Prompt Injection
  • AML.T0056 — Rogue ML Services
  • AML.T0040 — ML Model Poisoning
  • AML.T0043 — Adversarial ML Attack
  • AML.T0048 — Exfiltration via ML Inference
  • AML.T0054 — Supply Chain Compromise
  • AML.T0058 — Backdoor ML Model
  • All remaining ATLAS tactics mapped
10 / 10

OWASP LLM Top 10 — 2025

  • LLM01 — Prompt Injection
  • LLM02 — Sensitive Information Disclosure
  • LLM03 — Supply Chain
  • LLM04 — Data and Model Poisoning
  • LLM05 — Improper Output Handling
  • LLM06 — Excessive Agency
  • LLM07 — System Prompt Leakage
  • LLM08 — Vector and Embedding Weaknesses
  • LLM09 — Misinformation
  • LLM10 — Unbounded Consumption
Regulatory

Compliance Frameworks

  • EU AI Act — High-Risk System Controls
  • NIST AI RMF — Govern, Map, Measure, Manage
  • ISO/IEC 42001 — AI Management Systems
  • GDPR — AI-driven personal data flows
  • NCSC AI Security Guidelines
  • SPARTA — Space/NTN threat taxonomy

Test Offensively. Defend in Production.

AI Shield is the defensive counterpart to NIGHTFALL. Every offensive finding from NIGHTFALL tools generates a real-time blocking rule in AI Shield. The feedback loop is bidirectional — running FORGE against a model produces AI Shield policy. Running ARSENAL against an agent produces runtime detection signatures.

Offensive Testing
NIGHTFALL
65-tool offensive framework
Findings Correlation
WARLORD
Autonomous campaign engine — aggregates findings
Runtime Defence
AI SHIELD
114-module autonomous defence platform
Enterprise SIEM
redspecter-siem
Splunk · Sentinel · QRadar
Governance
IDRIS
AI asset discovery and governance
Reporting
OVERWATCH
Consolidated posture reporting

Deploy Anywhere

AI Shield modules are available as Docker containers (UBI9 certified), Python packages via PyPI, and as Kubernetes Helm charts. All deployment paths produce the same module behaviour — consistent signatures, consistent latency, consistent API surface.

Docker
docker pull
PyPI
pip install
Kubernetes
Helm chart
Red Hat UBI9
Certified image
Azure
Container instances
AWS
ECS / EKS
GCP
Cloud Run / GKE
Air-Gap
Offline install
REST API
OpenAPI 3.1

Deployment Notice

Red Specter AI Shield is an authorised security product intended for deployment on systems you own or are contractually authorised to protect. AI Shield modules operate in monitoring and blocking modes — ensure that blocking mode deployment is authorised by your organisation's change management process before activation. Module behaviour must be validated against your specific AI agent deployment before production rollout. All modules operate under Apache License 2.0. Red Specter Security Research Ltd accepts no liability for incidents arising from misconfiguration or unauthorised deployment.

Deploy AI Shield
Start Defending Your AI Fleet

114 modules. 17 verticals. Real-time threat detection across your entire AI agent deployment. Launch the GUI to see your fleet status, or read the documentation to begin a self-hosted deployment.

Launch AI Shield GUI Read Documentation