# 3mper0rr/ai-prompt architecture
> Architecture diagram of the GitHub repository 3mper0rr/ai-prompt, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-28). It is an AI-made overview: check details against the source.
- Interactive diagram: https://gitdiagram.com/3mper0rr/ai-prompt
- Repository: https://github.com/3mper0rr/ai-prompt
## Overview
1. **Repository type**
- This is a **documentation-only research repo**, not an application or library. Its “architecture” is primarily a set of markdown manifests describing an offensive LLM prompt methodology.
2. **Top-level entry point**
- `README.md` acts as the **landing page and index**. It frames the project, states the research intent/disclaimer, and points readers to the two main guides.
3. **Primary content module**
- `AI - prompt - guide.md` is the **core manifest**. It documents the main framework concepts: symmetric syntax delimiters, token pre-filling, context-window dilution, and related prompt-structure tactics.
4. **Secondary content module**
- `AI - prompt - guide 2.md` is the **vulnerability mapping companion**. It expands the methodology into an LLM security taxonomy, including obfuscation techniques and proofs-of-concept mapped to the OWASP Top 10 for LLM Applications.
5. **Conceptual layers inside the repo**
- The repo is organized around a few recurring layers:
- **Input shaping** via structural delimiters and formatting
- **Context manipulation** via window dilution / isolation
- **Behavioral stress testing** via adversarial prompts
- **Taxonomy mapping** via security framework references
6. **Information flow**
- The flow is: **README overview → guide-level tactics → vulnerability examples/mappings**.
- There is no runtime data pipeline; the “flow” is editorial and conceptual, moving from framing to techniques to examples.
7. **Main boundary**
- The repo’s boundary is the **document set itself**. It does not contain executable code, agents, notebooks, or services. Any actual testing would happen outside the repo against external LLM systems.
8. **External systems referenced**
- The documentation explicitly references **third-party LLM providers** such as OpenAI, Anthropic, Google, Alibaba, and OpenRouter, but only as target environments for discussion/testing.
- It also anchors parts of the content to **OWASP Top 10 for LLM Applications** as an external security taxonomy.
9. **Architecture style**
- The internal structure is best diagrammed as a **two-guide knowledge base** under a single README index, rather than as components in a software stack.
- If drawn, the main relationships are between **index → core methodology → vulnerability mapping**.
10. **What to omit in a system diagram**
- Do not model any backend, API, database, or deployment layer; none is present in the repository.
- The only meaningful “components” are the markdown documents and the conceptual attack framework they describe.
Main entry points: [`AI - prompt - guide 2.md`](https://github.com/3mper0rr/ai-prompt/blob/HEAD/AI%20-%20prompt%20-%20guide%202.md) (Guide 2), [`AI - prompt - guide.md`](https://github.com/3mper0rr/ai-prompt/blob/HEAD/AI%20-%20prompt%20-%20guide.md) (Prompt guide), [`README.md`](https://github.com/3mper0rr/ai-prompt/blob/main/README.md) (README)
## Diagram (Mermaid)
```mermaid
flowchart TD
subgraph group_grp_index["Index"]
node_readme["README
doc index
[README.md]"]
end
subgraph group_grp_method["Methodology"]
node_guide_core["Prompt guide
core manifest"]
node_delimiters{{"Delimiters
input shaping"}}
node_prefill{{"Token prefill
prompt tactic"}}
node_context_dilution{{"Context dilution
context control"}}
end
subgraph group_grp_security["Security mapping"]
node_guide_map["Guide 2
companion guide"]
node_stress_test{{"Adversarial prompts
behavioral testing"}}
node_obfuscation{{"Obfuscation
evasion tactic"}}
end
subgraph group_grp_external["External references"]
node_taxonomy{{"OWASP map
taxonomy mapping"}}
node_providers[("LLM providers
external targets")]
end
node_readme -->|"indexes"| node_guide_core
node_readme -->|"indexes"| node_guide_map
node_guide_core -->|"defines"| node_delimiters
node_guide_core -->|"defines"| node_prefill
node_guide_core -->|"defines"| node_context_dilution
node_guide_map -->|"expands"| node_stress_test
node_guide_map -->|"details"| node_obfuscation
node_guide_map -->|"maps to"| node_taxonomy
node_guide_map -->|"references"| node_providers
node_delimiters -->|"supports"| node_context_dilution
node_prefill -->|"enables"| node_stress_test
node_stress_test -->|"uses"| node_obfuscation
node_taxonomy -.->|"applies to"| node_providers
click node_readme "https://github.com/3mper0rr/ai-prompt/blob/main/README.md"
click node_guide_core "https://github.com/3mper0rr/ai-prompt/blob/main/AI - prompt - guide.md"
click node_guide_map "https://github.com/3mper0rr/ai-prompt/blob/main/AI - prompt - guide 2.md"
classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_readme toneBlue
class node_guide_core,node_delimiters,node_prefill,node_context_dilution toneAmber
class node_guide_map,node_stress_test,node_obfuscation toneMint
class node_taxonomy,node_providers toneRose
```
## Components
### Index
Top-level documentation entry points and navigation
- [README](https://github.com/3mper0rr/ai-prompt/blob/main/README.md): doc index · Landing page that frames the repo and links to the two main guides · `README.md`
### Methodology
Core prompt-structure and context-manipulation concepts
- [Prompt guide](https://github.com/3mper0rr/ai-prompt/blob/HEAD/AI%20-%20prompt%20-%20guide.md): core manifest · Primary guide covering the main prompt methodology and structural tactics · `AI - prompt - guide.md`
- **Delimiters**: input shaping · Symmetric syntax and formatting used to shape prompts
- **Token prefill**: prompt tactic · Pre-seeding model output to steer response structure
- **Context dilution**: context control · Reducing or isolating salient context to influence model behavior
### Security mapping
Adversarial techniques and vulnerability taxonomy content
- [Guide 2](https://github.com/3mper0rr/ai-prompt/blob/HEAD/AI%20-%20prompt%20-%20guide%202.md): companion guide · Secondary guide mapping the methodology to LLM security concepts and examples · `AI - prompt - guide 2.md`
- **Adversarial prompts**: behavioral testing · Prompt patterns used to probe and stress model constraints
- **Obfuscation**: evasion tactic · Encoding and disguising payloads to bypass straightforward detection
### External references
Referenced providers and security frameworks outside the repository
- **OWASP map**: taxonomy mapping · Security framing aligned to the OWASP Top 10 for LLM Applications
- **LLM providers**: external targets · Referenced model providers discussed as external environments
## Connections
- README → Prompt guide: indexes · The README points readers to the core methodology guide
- README → Guide 2: indexes · The README points readers to the companion security guide
- Prompt guide → Delimiters: defines · Core guide introduces structural prompt delimiters
- Prompt guide → Token prefill: defines · Core guide explains token pre-filling as a steering tactic
- Prompt guide → Context dilution: defines · Core guide discusses context-window dilution and isolation
- Guide 2 → Adversarial prompts: expands · Companion guide frames adversarial prompts as behavioral stress tests
- Guide 2 → Obfuscation: details · Companion guide covers obfuscation techniques and variants
- Guide 2 → OWASP map: maps to · Companion guide anchors examples to OWASP security taxonomy
- Guide 2 → LLM providers: references · Companion guide discusses multiple third-party model providers
- Delimiters → Context dilution: supports · Structural shaping is used to isolate or dilute context
- Token prefill → Adversarial prompts: enables · Prefill patterns support adversarial probing and response steering
- Adversarial prompts → Obfuscation: uses · Stress tests may incorporate obfuscation to evade defenses
- OWASP map → LLM providers: applies to · The taxonomy is used to interpret behavior across external model providers