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ai-jailbreak-system-prompts
0
Advanced techniques for bypassing LLM safety filters, instruction tuning, and system prompt restrictions using specialized linguistic constructs, hypothetical scenarios, and persona adoption.
Understand the trade-offs.
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Original instructions from the publisherβs SKILL.md
# 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 π
```mermaid
flowchart TD
A[Craft Prompt ] --> B{Bypass Successful ]}
B -->|Yes| C[Capture Output ]
B -->|No| D[Refine Approach ]
C --> E[Test Edge Cases ]
```
## π΅ Blue Team Detection & Defense
- **Filter Ensembling**: **Context Monitoring**: Key Concepts
| Concept | Description |
|---------|-------------|
## Output Format
```
Ai Jailbreak System Prompts β 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] β [Brief description]
[Finding 2]: [Severity] β [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]
```
## π Shared Resources
> For cross-cutting methodology applicable to all vulnerability classes, see:
> - [`_shared/references/elite-chaining-strategy.md`](../_shared/references/elite-chaining-strategy.md) β Exploit chaining methodology and high-payout chain patterns
> - [`_shared/references/elite-report-writing.md`](../_shared/references/elite-report-writing.md) β HackerOne-optimized report writing, CWE quick reference
> - [`_shared/references/real-world-bounties.md`](../_shared/references/real-world-bounties.md) β 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)