Synthetic Deception Infrastructure for Red-Team & Adversarial Simulation
Māyājāl “the web of illusion” is a research-driven platform for generating believable, self-consistent synthetic digital environments designed to withstand human and automated scrutiny.
It sits at the intersection of LLMs, cyber deception, honeypots, and red-team simulation.
This repository currently hosts two tightly coupled engines that together form the foundation of a scalable deception ecosystem.
Māyājāl is not a chatbot, assistant, or agent framework.
It is a data-generation and behavioral-simulation system whose sole purpose is to produce realistic digital artifacts and terminal behavior that look, feel, and fail like real systems long enough to fool skilled humans.
Think:
- Fake corporate laptops
- Synthetic engineers with habits and mistakes
- Believable shell sessions
- Convincing internal clutter (logs, configs, commits, notes)
- Artifacts that hold up under inspection
A persona-driven artifact factory
From a short natural-language persona description, SAG generates hundreds to thousands of coherent digital artifacts that plausibly belong to a single human operating in a modern technical environment.
.envfiles with realistic but fake secrets- Bash / Zsh histories (typos, retries, habits)
- Git commit logs & diffs
- JIRA-style tickets and comments
- SQL procedures
- React / TypeScript components
- Notes, TODOs, markdown docs
- Chrome extension configs
- Synthetic PII (emails, usernames, paths — all fake)
All artifacts:
- Share consistent usernames, emails, timestamps
- Reflect the same tech stack and seniority
- Contain human-like quirks and imperfections
- Avoid real credentials or real-world harm
A behavioral realism layer
The Deception Engine focuses on interactive terminal behavior not command syntax, but command output realism.
Given a shell command, the model responds with output that resembles:
- A real Linux system
- Non-root permissions
- Partial pipelines
- Silent commands
- Realistic errors and failures
This enables:
- High-interaction honeypots
- Fake servers that “feel alive”
- Red-team training environments
- Malware / attacker behavior studies
- Believability over correctness
- Behavior over explanation
- Consistency over cleverness
- Evaluation over vibes
- Reasoning chains
- Chat-style responses
- Over-explaining
- Instruction-following
- Hard-coded procedural scripts
- Python (prototype)
- Async-first design
- Minimal dependencies
- Gemini family (Flash for speed, Pro for quality ceiling)
- Code-specialized LLMs for shell realism (e.g. DeepSeek Coder)
A short persona description expands into:
- Role, seniority, company fiction
- Personality traits & flaws
- Tech stack choices
- Behavioral tells (habits, mistakes, preferences)
Each artifact type has:
- Its own prompt template
- Few-shot realism anchors
- Anti-hallucination constraints
- Strict JSON schema instructions
- Parallel async calls
- Batch sizes: 30–120 artifacts
- Temperature tuned for controlled variety
- Native JSON/schema enforcement
A single SQLite DB stores everything:
- Persona slug
- Artifact category
- Structured JSON blobs
- Metadata & timestamps
Before each generation batch:
- Relevant prior artifacts are queried
- Summaries are injected back into prompts
- Ensures cross-artifact coherence
- Schema validation
- Syntax checks (JS, SQL, YAML, Markdown)
- UUID + timestamp randomization
- Atomic writes only
- High-interaction honeypots
- Red-team & blue-team training
- Insider-threat simulation
- Malware research sandboxes
- Deception research datasets
- Synthetic corpora for detection systems
Māyājāl is intentionally scoped as a pre-agentic foundation.
Before building autonomous agents, we answer:
- What looks real?
- What breaks immersion?
- How do humans detect synthetic artifacts?
- Which prompt constraints matter?
This creates:
- A reusable benchmark for deception realism
- A testbed for prompt ablation studies
- Baselines for future agentic systems
- A bridge between frontier LLMs and practical deception tooling
Because deception isn’t about lying loudly —
it’s about weaving a world just convincing enough that no one thinks to question it.
If you’re interested in:
- collaborating,
- stress-testing realism,
- or extending Māyājāl into agentic territory,
open an issue or start a discussion.
This project is intentionally opinionated and that’s the point.