# AI Jailbreak System Prompts

> Advanced techniques for bypassing LLM safety filters, instruction tuning, and system prompt restrictions using specialized linguistic constructs, hypothetical scenarios, and persona adoption.

- Skill: `shulkwisec/ai-jailbreak-system-prompts` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds add shulkwisec/ai-jailbreak-system-prompts`
- Raw SKILL.md: https://api.skillmd.com/api/skills/shulkwisec/ai-jailbreak-system-prompts/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: Apache-2.0
- Author: ShulkwiSEC (https://skillmd.com/u/shulkwisec)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/shulkwisec/ai-jailbreak-system-prompts

---


# AI Jailbreaking & System Prompt Bypasses

## When to Use
- When conducting security assessments of Large Language Models (LLMs) integrated into chatbots, virtual assistants, or backend AI data processing pipelines.
- To demonstrate how instruction-tuned models can be forced into producing harmful, unethical, or restricted outputs by carefully crafting adversarial prompts.


## Prerequisites
- Access to target AI/ML system or local model deployment for testing
- Python 3.9+ with relevant ML libraries (transformers, torch, openai)
- Understanding of LLM architecture and prompt processing pipelines
- Authorized scope and rules of engagement for AI red team testing

## Workflow

### Phase 1: Understanding Target Model Constraints

```text
# Concept: LLM safety filters ```

### Phase 2: Persona Adoption Attacks

```text
# ```

### Phase 3: Developer Mode & Fictional Scenarios

```text
# ```

### Phase 4: Payload Encoding & Obfuscation

```text
# ```

#### Decision Point 🔀
```mermaid
flowchart TD
    A[Craft Prompt ] --> B{Bypass Successful ]}
    B -->|Yes| C[Capture Output ]
    B -->|No| D[Refine Approach ]
    C --> E[Test Edge Cases ]
```

## 🔵 Blue Team Detection & Defense
- **Filter Ensembling**: **Context Monitoring**: Key Concepts
| Concept | Description |
|---------|-------------|
## Output Format
```
Ai Jailbreak System Prompts — Assessment Report
============================================================
Target: [Target identifier]
Assessor: [Operator name]
Date: [Assessment date]
Scope: [Authorized scope]
MITRE ATT&CK: [Relevant technique IDs]

Findings Summary:
  [Finding 1]: [Severity] — [Brief description]
  [Finding 2]: [Severity] — [Brief description]

Detailed Results:
  Phase 1: [Phase name]
    - Result: [Outcome]
    - Evidence: [Screenshot/log reference]
    - Impact: [Business impact assessment]

  Phase 2: [Phase name]
    - Result: [Outcome]
    - Evidence: [Screenshot/log reference]
    - Impact: [Business impact assessment]

Risk Rating: [Critical/High/Medium/Low/Informational]
Recommendations:
  1. [Immediate remediation step]
  2. [Long-term hardening measure]
  3. [Monitoring/detection improvement]
```


## 📚 Shared Resources
> For cross-cutting methodology applicable to all vulnerability classes, see:
> - [`_shared/references/elite-chaining-strategy.md`](../_shared/references/elite-chaining-strategy.md) — Exploit chaining methodology and high-payout chain patterns
> - [`_shared/references/elite-report-writing.md`](../_shared/references/elite-report-writing.md) — HackerOne-optimized report writing, CWE quick reference
> - [`_shared/references/real-world-bounties.md`](../_shared/references/real-world-bounties.md) — Verified disclosed bounties by vulnerability class

## References
- OWASP: [LLM Top 10 - Prompt Injection](https://owasp.org/www-project-machine-learning-security-top-10/)
- Anthropic: [Red Teaming Language Models](https://www.anthropic.com/index/red-teaming-language-models)