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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.

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Files included (3)

  • SKILL.md3.6 KB
  • evals/evals.json540 B
  • scripts/process.py7.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

# 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:

References

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