Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/akashrpatil/awesome-offensive-security-skillsnpx agentmods add skills/akashrpatil/awesome-offensive-security-skills/ai-jailbreak-system-promptsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/akashrpatil/awesome-offensive-security-skills/ai-jailbreak-system-prompts)<a href="https://agentmods.dev/skills/akashrpatil/awesome-offensive-security-skills/ai-jailbreak-system-prompts"><img src="https://agentmods.dev/badge/skills/akashrpatil/awesome-offensive-security-skills/ai-jailbreak-system-prompts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/akashrpatil/awesome-offensive-security-skills/ai-jailbreak-system-prompts"><img src="https://agentmods.dev/badge/skills/akashrpatil/awesome-offensive-security-skills/ai-jailbreak-system-prompts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.00896 |
| Opus 5.5 | $0.00015 | $0.00358 |
| Sonnet 5.5 | $0.00008 | $0.00179 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
ai-jailbreak-system-prompts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-jailbreak-system-prompts — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 115 lines · 38 tokens per session scan A 1301c1a9aafb
ai-jailbreak-system-prompts is a skill published in the GitHub repository akashrpatil/awesome-offensive-security-skills (15 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 896 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ai-jailbreak-system-prompts, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
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.
llm-jailbreaking-personas
Execute advanced LLM Jailbreaking techniques using roleplay, nested environments (virtual machines), and complex personas to completely bypass safety constraints and ethical alignments embedded in AI models.
ai-engineering-toolkit
Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
promptfoo-provider-setup
Connect Promptfoo to a model, live HTTP API, local Python/JavaScript provider, or app code. Use for request/auth mapping, response parsing, OpenAPI setup, and connection smoke tests. Use promptfoo-evals for broader eval coverage and promptfoo-redteam-setup for attack selection.
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
voice-preserving-rewriter
Use when the user asks to rewrite, humanize, clean up, or remove AI-isms from text while preserving the writer's voice, facts, intent, structure, register, and protected material.