NEW: BrainGuard™ Engine - Detect Cognitive Degradation Before Permanent Damage
Your AI Needs an Immune System
Detect Vulnerabilities Through Architecture Analysis—Before Attacks Happen
Introducing SPR{k³ and its revolutionary BrainGuard™ Engine. Unlike traditional security tools that react to known threats, SPR{k³ analyzes your AI's architectural patterns to detect vulnerabilities before they can be exploited. Inspired by biological immune systems, it proactively identifies weaknesses in code structure, agent behavior, and LLM integration—stopping backdoors, brain rot, and architectural decay before permanent damage occurs.
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First 50 Users: 25% Lifetime Discount
Protecting $15B+ in AI Infrastructure
Problem/Solution Matrix
Backdoor Risk
AI agent backdoors persist even with 20x clean data.
Brain Rot
LLMs experience 'brain rot' from 70% junk data.
Small-Sample Attack
Just 250 samples can backdoor any model.
Production Bugs
Production bugs cost $50-100K per incident.
Refactor Failure
85% of refactoring projects fail.
Built on Cutting-Edge Research
SPR{k³ is not just a product; it's a platform forged in the crucible of peer-reviewed scientific discovery. Our innovations are directly informed by groundbreaking research, ensuring a robust, forward-thinking defense against the most sophisticated AI vulnerabilities. Here are some of the foundational papers:
"Malice in Agentland" (Boisvert et al., October 2025)
  • Shows 2% poisoned data → 80% attack success
  • Backdoors persist through fine-tuning
  • SPR{k³ implements all recommended defenses
"LLM Brain Rot" (arXiv:2510.13928)
  • 17.7% permanent performance loss from junk data
  • Thought-skipping as primary failure mode
  • BrainGuard prevents degradation
"250-Sample Attack" (arXiv:2510.07192v1)
  • Constant samples bypass percentage defenses
  • SPR{k³ detects at 1-50 file threshold
  • First platform with multi-stage detection
The SPR{k³ Paradigm
Inspired by SRPK3 Kinase Enzymes that Regulate Cellular Survival
Our bio-inspired approach uses the same principles that cells use to identify and preserve critical functions:
Pattern Recognition
Like cellular receptors identifying threats
Survival Analysis
Patterns that persist through evolution are preserved
Kinase Activation
Rapid response to emerging threats
Immune Memory
Learning from past attacks
Multi-Engine Detection Architecture
Specialized engines including distributed training, agent security, document injection, and supply chain detection. These detection and learning engines form the technical core of SPR{k³ and work in concert to analyze and safeguard your AI architecture:
Bio-Intelligence
Survival pattern analysis, load-bearing code identification, temporal persistence tracking, based on drug discovery research.
Temporal Intelligence
Git history analysis, velocity tracking (spread rate), anomaly detection via z-scores, pattern evolution monitoring.
Structural Intelligence
Dependency graph analysis, blast radius calculation, architectural boundary detection, cross-service impact mapping.
Predictive Intelligence
Future pattern projection, risk trajectory modeling, proactive recommendation systems, evolutionary pathway analysis.
The SPR{k³ Core Engine
At its core, SPR{k³ introduces a revolutionary, bio-inspired pattern detection system that redefines how we perceive and interact with codebases. Moving beyond the conventional view of code as static text, our engine processes it as a dynamic, living ecosystem where patterns continuously grow, spread, and adapt over time. This unique approach unlocks unprecedented insights into the health, evolution, and security of your software architecture.
Comprehensively Scans Your Architecture
  • Automatically detects recurring code patterns across 7+ programming languages (Python, JavaScript, Java, C++, Go, Rust, Ruby).
  • Maps intricate structural relationships and dependencies with advanced semantic understanding.
  • Tracks the complete lifecycle of every identified pattern throughout your Git history.
  • Clearly defines architectural boundaries and inter-service relationships.
Precisely Tracks Temporal Evolution
  • Records precise timestamps for the initial emergence of each pattern.
  • Monitors the propagation of patterns across files and services as they evolve over time.
  • Calculates critical metrics such as velocity (files per day) and acceleration.
  • Identifies distinct contribution patterns and developer behavioral trends.
Intelligently Analyzes Structural Significance
  • Interprets the semantic meaning of code elements (e.g., distinguishing authorization from timeout configurations).
  • Calculates potential "blast radius" and impact of changes on downstream dependencies.
  • Automatically detects and flags architectural boundary violations.
  • Maps critical bridge zones connecting microservices and components.
Leverages Bio-Inspired Intelligence
  • Identifies "survivor patterns" – robust code patterns that persist despite multiple refactoring attempts.
  • Effectively distinguishes between optimized solutions and accumulating technical debt.
  • Applies evolutionary principles, initially derived from advanced drug discovery research, to code analysis.
  • Utilizes an inverted preservation paradigm for highly accurate anomaly detection, mirroring biological immunity.
A Single, Unified Co-ordinated Process
This single, unified process with multi layer analysis is the foundational powerhouse behind every SPR{k³ capability. From delivering precise architectural recommendations to detecting subtle security threats, it ensures unparalleled consistency, efficiency, and depth across the entire platform.

Bio-Inspired Methodology for Deeper Insight
Drawing on cutting-edge research, including principles from pharmaceutical studies on drug compound survival, our bio-inspired methodology is uniquely capable of discerning which patterns represent valuable architectural optimizations that should be preserved, versus those that signal critical threats like security vulnerabilities or growing technical debt.
Application 1: Architecture Intelligence
The Same Engine Answers: "How should we improve this codebase?"
The Architecture Intelligence application leverages SPR{k³'s core engine to deliver actionable insights into your codebase structure. It goes beyond simple code duplication to understand which patterns serve critical architectural functions (and should be preserved) versus those representing genuine technical debt requiring remediation.
Through sophisticated analysis of pattern evolution, the system traces how architectural decisions propagate, calculates precise security and maintenance implications, quantifies refactoring effort, and provides ROI-justified recommendations.
Root Cause Analysis
"Authorisation scattered across 8 files = 47 security vulnerabilities"
Technical Debt Quantification
"854 hours of architectural debt = £85,400"
Refactoring ROI
Prioritises fixes by blast radius and business impact
Survivor Pattern Recognition
Preserves patterns that are architecturally optimised
Cross-Language Analysis
Unified intelligence across your entire stack

Example Detection
Pattern: DATABASE_TIMEOUT = 5000
├─ Occurrences: 47 files
├─ First seen: 3 years ago
├─ Contributors: 12 developers
├─ Architectural role: Configuration layer
├─ Blast radius: High (affects 23 services)
└─ Classification: Survivor pattern
 (survived 6 refactorings)
The Architecture Intelligence Workflow
SPR{k³ streamlines architectural optimization with a clear, data-driven process, distinguishing itself from traditional static analysis tools. Unlike basic linters or code duplication detectors, our system performs a deep semantic and historical analysis to understand the *intent* and *evolution* of code patterns. From initial codebase ingestion to actionable recommendations, it transforms raw code into strategic insights that reflect genuine architectural health rather than surface-level issues.
Codebase Scan
Comprehensive scan of your entire codebase, supporting multiple languages and VCS. Constructs Abstract Syntax Trees (ASTs) and extracts metadata like dependency graphs, commit history, and author attribution for deep analysis.
Pattern ID
Identifies recurring code patterns semantically, distinguishing architectural constructs ("survivor patterns") from technical debt. Analyzes historical context and evolution to map propagation and architectural criticality.
Impact Analysis
Quantifies security and maintenance implications of technical debt. Calculates refactoring effort and provides ROI-justified assessment by correlating architectural health with business impacts like deployment frequency and defect rate.
Recommendations
Delivers prioritized, actionable recommendations tailored to your organization. Focuses remediation efforts on areas with greatest strategic value, ranging from specific code refactoring to architectural restructuring proposals.
This systematic approach ensures every recommendation is backed by a deep understanding of your codebase's structure, evolutionary history, and potential business impact, moving beyond simple code duplication to true architectural intelligence.
Key Outcomes & Business Value
  • Reduced Technical Debt: Proactively identify and address architectural weaknesses.
  • Improved Security Posture: Pinpoint and remediate security-critical patterns.
  • Enhanced Maintainability & Scalability: Optimize codebase for easier maintenance and faster development.
  • Quantifiable ROI on Refactoring: Prioritize architectural improvements based on clear business impact.
  • Accelerated Developer Velocity: Remove friction points and cognitive load for engineering teams.

Seamless Integration into Workflows
SPR{k³ integrates frictionlessly with your existing CI/CD pipelines, version control systems, and project management tools. It delivers continuous architectural monitoring and real-time feedback, empowering data-driven decisions and transforming raw code into a strategic asset for accelerated innovation and reduced operational risks.
Application 2: Security Intelligence (Sentinel)
The Same Engine Answers: "Is this a coordinated attack?"
Sentinel represents a paradigm shift in ML security. Whilst traditional security tools analyze model outputs or training data after the fact, Sentinel analyzes code architecture using the same engine that understands your codebase structure. This architectural context enables detection of poisoning attacks during code commits—before models are ever trained. Traditional ML security often relies on post-deployment monitoring, reactive measures, or shallow analysis of data and model artifacts, missing the root cause of many vulnerabilities embedded deep within the development lifecycle. Sentinel's approach, however, dives into the underlying code, understanding the intent and interdependencies of architectural components, allowing for proactive defense against sophisticated threats.
Unlike conventional methods that might flag suspicious data points or unusual model behavior, Sentinel operates earlier in the pipeline. It doesn't just look at *what* the model does, but *how* it's built, scrutinizing the architectural choices and code patterns that define its integrity. This enables it to identify malicious interventions or design flaws that could lead to poisoning attacks long before they manifest in deployed models, saving significant remediation costs and preventing potential breaches.
Sentinel's 4-Stage Detection System Overview:
The multi-stage detection system escalates through four distinct phases, each providing increasingly sophisticated analysis:
Stage 1: Initial Anomaly
Catches obvious malicious patterns in individual files, acting as the first line of defense.
Stage 2: Velocity Anomaly
Identifies velocity anomalies as suspicious patterns begin spreading unnaturally across the codebase.
Stage 3: Structural Violation
Recognizes structural violations as coordinated attacks scale and disrupt architectural integrity.
Stage 4: Coordinated Poisoning
Confirms large-scale, coordinated poisoning based on research-validated thresholds and sophisticated analysis.
BrainGuard™: Preventing LLM Cognitive Degradation
BrainGuard™ is Sentinel's specialized LLM cognitive health monitoring system. It provides an unparalleled advance warning capability, ensuring proactive intervention before critical failures occur.
BrainGuard™ employs a sophisticated four-layer cognitive protection system:
Reasoning Chain Integrity Analysis
This layer meticulously analyzes the integrity of an LLM's internal reasoning chains, identifying logical inconsistencies or breakdowns that indicate cognitive degradation.
Quality Dosage Tracking
It tracks the "dosage" of high-quality data consumed by the LLM over time, flagging insufficient or compromised input that could lead to performance erosion.
Capability Drift Detection
This layer monitors for subtle shifts in the LLM's core capabilities, detecting "drift" away from intended functions or a decline in specific cognitive tasks.
Recovery Protocol System
In the event of detected degradation, this system initiates predefined recovery protocols, ranging from targeted retraining to model recalibration, to restore optimal function.

BrainGuard™ can detect "thought-skipping" in degrading models—where an LLM might jump to conclusions without fully processing intermediate steps—long before it impacts user-facing applications.

By preventing such model failures, BrainGuard™ offers significant cost savings, avoiding expensive downtime, reputational damage, and the extensive resources required for post-failure remediation.
Sentinel's Multi-Stage Detection
Sentinel employs a sophisticated four-stage detection system, leveraging SPR{k³'s architectural intelligence to identify and escalate ML poisoning attacks as they evolve within your codebase. This proactive approach catches threats long before they impact models.
Initial Anomaly
Detection of a malicious pattern in a single file, indicating an isolated threat. This early warning sign is often subtle, but critical for rapid response.
Velocity Anomaly
The malicious pattern rapidly spreads across 25-50 files, indicating intentional propagation. This abnormal "code velocity" triggers an escalation in threat assessment.
Structural Violation
Significant architectural boundaries are breached, signaling a deeper, more systemic attack. Malicious logic attempts to embed across multiple components, increasing blast radius.
Coordinated Poisoning
Large-scale poisoning affecting 250+ files is confirmed, representing a critical and widespread attack. Sentinel correlates data to identify coordinated efforts based on research-validated thresholds.
Each stage provides increasingly sophisticated analysis, allowing for precise identification and mitigation of threats as they attempt to infiltrate your ML pipeline.
The Architecture Connection: Why SPR{k³ Sentinel Is Unique
Traditional ML security tools operate in a vacuum, analysing models or datasets without understanding the codebase context. SPR{k³ Sentinel fundamentally changes this paradigm by applying architectural intelligence to security detection. Because the platform already understands your code structure, normal development patterns, and architectural boundaries, it can identify anomalies that other tools miss entirely.
Limited code visibility
Full codebase context
Post-deployment detection
Real-time & pre-deploy detection
The platform advantage is clear: SPR{k³ already knows your architecture intimately, enabling it to detect security threats that manifest as architectural anomalies. This is intelligence that traditional security tools—lacking codebase context—simply cannot provide.
What Sentinel Detects
Anomalous Structural Changes
"This pattern doesn't match your established architectural conventions and boundaries"
Velocity Anomalies
"This pattern is spreading at 125 files/day—far too fast for normal development (2/month baseline)"
Contributor Anomalies
"One developer shouldn't be simultaneously modifying 250 files across multiple services"
Architectural Violations
"These changes break your normal service boundaries and introduce cross-cutting concerns"
How The Platform Works
Deploying SPR{k³ is straightforward, with the platform designed to integrate seamlessly into your existing development workflow. The core engine first establishes a comprehensive baseline of your architecture, then continuously monitors for patterns that deviate from normal behaviour.
1. Deploy the Core Engine
The engine learns your architectural baseline, including pattern inventory, normal velocity, contributor patterns, and service boundaries.
sprk3 init /your/codebase
2. Choose Applications
Select your desired mode for architecture optimisation, security monitoring, or both for full intelligence.
# Architecture optimisation
sprk3 analyze --architecture

# Security monitoring
sprk3 sentinel --monitor

# Both simultaneously
sprk3 analyze --full-intelligence
3. Continuous Monitoring
Receive ongoing intelligence and alerts. Architecture Mode provides quarterly scans, Security Mode offers 24/7 monitoring and instant alerts, and Integrated Mode combines both.
  • Architecture Mode: Quarterly scans, ROI reports
  • Security Mode: 24/7 monitoring, instant alerts
  • Integrated Mode: Complete intelligence

Key Benefit: SPR{k³ provides seamless integration and continuous, intelligent monitoring of your codebase, detecting architectural anomalies and security threats in real-time.
Use Cases: Architecture + Security Together
Scenario 1: Development Team
Uses Architecture Mode quarterly for refactoring planning. Discovers scattered authorisation logic and adds Sentinel to monitor for security drift, receiving immediate alerts.
Scenario 2: ML Team
Deploys Sentinel for 24/7 poisoning detection. Detects suspicious patterns spreading rapidly. Uses Architecture Mode to understand structural impact and prevent compromise of downstream services.
Scenario 3: Enterprise
Runs both modes simultaneously for comprehensive intelligence. Identifies critical structural points and prioritises monitoring. Leadership gains unified visibility of technical debt and real-time security posture.
Key Differentiators & Competitive Advantages
SPR{k³'s unique architecture and bio-inspired methodology deliver capabilities that simply don't exist in traditional tools. The platform's ability to understand both code structure and temporal evolution creates a new category of software intelligence.
One Core Engine, Two Applications
Maximises technology leverage by using a single sophisticated pattern detection engine to power both architectural optimisation and security detection—unprecedented efficiency in codebase intelligence.
Bio-Inspired Pattern Recognition
Applies evolutionary principles from drug discovery research to distinguish survivor patterns (architectural optimisations) from threats (security attacks, technical debt)—a methodology derived from pharmaceutical compound analysis.
Cross-Language Intelligence
Provides unified architectural understanding across 7+ programming languages (Python, JavaScript, Java, C++, Go, Rust, Ruby), enabling enterprise-wide pattern analysis regardless of technology stack diversity.
Temporal Evolution Tracking
Monitors pattern velocity and acceleration through complete Git history analysis, enabling early detection of anomalies and projection of attack trajectories before they reach critical thresholds.
Patent-Protected Methodology
Comprehensive intellectual property protection filed October 2025, covering the core architectural intelligence methodology, multi-stage detection system, and inverted preservation paradigm.
Multi-Stage Detection System
Escalates from single-file content analysis through velocity anomalies to structural violations and research-validated critical thresholds—providing layered defence with appropriate urgency at each stage.
Products, Proof Points & Technology
What We Do
We scan ML codebases and find vulnerabilities.
How It Works
01
Scan
Point at your ML codebase
02
Detect
Multi-engine analysis finds vulnerabilities
03
Report
Prioritized findings with fix guidance
04
Repeat
Schedule nightly, weekly, or on-demand
Coming Soon
🚀 CI/CD Safety Gates — Block vulnerable code before deploy
🚀 Compliance Reports — EU AI Act, SOC 2 audit evidence
Proof Points
Confirmed vulnerabilities
found in production ML systems
Low false positive rate
High-precision detection you can trust
Validated by
Meta, Microsoft, Google, NVIDIA, Amazon, HuggingFace
95.7% accuracy
vs ~70% industry standard
Novel attack vectors
Discovers vulnerabilities other tools miss
Patent Protection
US Provisional Filed October 8, 2025
"Architectural Significance Analysis and Pattern Preservation Intelligence System"
Transform Your Codebase Intelligence Today
The Platform Advantage
SPR{k³ represents a fundamental breakthrough in how we understand and secure codebases. By leveraging a single sophisticated engine to power both architectural optimisation and security detection, the platform delivers unprecedented intelligence that traditional siloed tools cannot match.

The unique insight: architectural patterns and security patterns are detected by the same underlying intelligence—pattern evolution, velocity anomalies, and structural significance. This unified approach provides technology leverage, architectural context, unified intelligence, cost efficiency, and superior detection accuracy.
Whether you're an engineering leader seeking to quantify and reduce technical debt, an architect optimising microservices boundaries, or a security engineer defending against ML poisoning attacks, SPR{k³ provides the intelligence you need to make informed decisions with confidence.
Trust Signals
Patent-protected architectural intelligence (filed October 2025)
Open-source core available on GitHub
35/35 tests passing, 85% code coverage
Built with enterprise-grade security standards
Production-ready deployment infrastructure
Bio-inspired methodology from pharmaceutical research
Research-validated detection (peer-reviewed arXiv papers)
One platform, two applications—proven technology leverage
Connect With Us
Bio-inspired intelligence for secure, optimized codebases