// TRILUX LAB v0.1 — OPEN RESEARCH
BUILDSYSTEMS
THATBREAK
BOUNDARIES.

An open R&D lab building AI security tools, jailbreak defenses, data integrity systems, and life-accelerating software. Built by the community. For everyone.

Open SourceCommunity DrivenZero Gatekeeping
// BUILT WITH THE COMMUNITY
GitHubHugging FaceOWASPDEF CONArXivOpenAI EvalsLangChainMITRE ATT&CKGitHubHugging FaceOWASPDEF CONArXivOpenAI EvalsLangChainMITRE ATT&CK
@0xsecMIT CSAIL@neuralriftStanford AI Lab@dataphreakerCarnegie Mellon@ghostbyteETH Zürich@hackernovaOxford ML@0xsecMIT CSAIL@neuralriftStanford AI Lab@dataphreakerCarnegie Mellon@ghostbyteETH Zürich@hackernovaOxford ML
// CORE RESEARCH

The research infrastructure behind next-gen AI safety

TRILUX LAB develops open-source frameworks for adversarial ML defense, data integrity verification, and automated threat intelligence — accelerating the global AI safety research pipeline by orders of magnitude.

0%
Attack surface coverage

Of known jailbreak vectors detected and blocked

0%
Detection accuracy over time

Sustained precision across adversarial datasets

0x
Research velocity multiplier

Faster than traditional security audit pipelines

// OUR PILLARS

Designed to break, defend, and rebuild.

[01]

LLM Jailbreak Defense

Neural guardrails, adversarial prompt detection, red-teaming frameworks.

Explore
[02]

GenAI Data Integrity

Poisoning detection, training data audits, provenance tracking.

Explore
[03]

Cyber Threat Intelligence

CVE monitoring, exploit analysis, zero-day research pipelines.

Explore
[04]
import { TriluxEngine } from '@trilux/core'import { SecurityModule } from '@trilux/shield'// Initialize defense frameworkconst engine = new TriluxEngine({mode: 'adversarial',modules: [SecurityModule],realtime: true,})await engine.deploy()123456789

Open Build Platform

Community tools, open APIs, shared research, no paywalls ever.

Explore

We're not building demos. We're not chasing hype. We're engineering the infrastructure that makes AI safe, honest, and human.

Our Manifesto

We believe in building technology that respects human agency. Every tool, framework, and research paper we produce is open, auditable, and designed to be used by anyone — researcher or hobbyist.

The AI safety problem won't be solved in closed labs. It requires a global, decentralized effort — thousands of contributors stress-testing, red-teaming, and refining systems in the open.

A
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By the founding team

Trilux Lab Research Division

// THE PROBLEMS WE ATTACK

Modern AI is broken in ways most people don't see yet.

[01]
Threat Vector

LLM Jailbreaks & Prompt Injection

Adversarial prompts can bypass safety filters, extracting harmful content or manipulating model behavior. Current guardrails are easily circumvented by sophisticated attacks.

Read Research
01 / 06
[02]
Threat Vector

Training Data Poisoning

Malicious actors can subtly corrupt training datasets, embedding backdoors or biases that persist through fine-tuning. Detection remains incredibly difficult at scale.

Read Research
02 / 06
[03]
Threat Vector

Model Hallucination at Scale

LLMs generate confidently wrong information that spreads through automated pipelines. Enterprise deployments amplify hallucinations into real-world decisions.

Read Research
03 / 06
[04]
Threat Vector

GenAI Privacy Leakage

Models memorize and regurgitate private data from training sets — PII, medical records, proprietary code. Extraction attacks grow more sophisticated daily.

Read Research
04 / 06
[05]
Threat Vector

AI-Powered Social Engineering

GenAI enables hyper-personalized phishing, deepfake generation, and automated manipulation campaigns at unprecedented scale and convincingness.

Read Research
05 / 06
[06]
Threat Vector

Black-box Model Accountability

When AI systems cause harm, tracing accountability through opaque architectures and distributed training pipelines is nearly impossible.

Read Research
06 / 06
THREAT DASHBOARD v2.1
THREAT LEVEL: HIGH
Last scan: 2m ago
CVETypeSeverity
CVE-2025-7841Prompt InjectionCRITICAL
CVE-2025-6502Data PoisoningHIGH
CVE-2025-5193Privacy LeakMEDIUM
CVE-2025-4827JailbreakHIGH
CVE-2025-3751HallucinationLOW
CVE-2025-2916Social Eng.CRITICAL
6 active threats monitored● LIVE
// OPEN TO ALL

Open-source AI safety, built by everyone.

TRILUX LAB is a living, breathing open-source community. No gatekeeping. No paywalls. If you see a problem in AI safety, come build the solution with us.

Researchers & Scientists
Security Engineers
AI/ML Engineers
Designers & Communicators
Technical Writers
Any human who gives a damn
GitHub RepoRead Manifesto
2,847 contributions in the last year● Active
RECENT COMMITS
▸fix: patch adversarial prompt bypass in v3.2
▸feat: add data provenance tracking module
▸docs: update red-teaming playbook
▸refactor: optimize threat detection pipeline
▸feat: implement zero-day CVE scanner
▸fix: resolve privacy leakage in embeddings
▸chore: update dependency security audit
▸feat: add community contribution dashboard
▸fix: false positive reduction in hallucination detector
▸docs: publish Q1 2025 research findings
▸fix: patch adversarial prompt bypass in v3.2
▸feat: add data provenance tracking module
▸docs: update red-teaming playbook
▸refactor: optimize threat detection pipeline
▸feat: implement zero-day CVE scanner
▸fix: resolve privacy leakage in embeddings
▸chore: update dependency security audit
▸feat: add community contribution dashboard
▸fix: false positive reduction in hallucination detector
▸docs: publish Q1 2025 research findings
// TEST THE LAB

Query our research systems in real time.

No smoke. No mirrors. Just results.

TRILUX TERMINAL v0.1
Domain:LLM Security
READY

Awaiting query█

AUTO-DEMO
Pipeline Status
Context loaded
Threat model applied
Generating response...
Validation passed
Execution Log