ai-prompt-leaking

Name: ai-prompt-leaking
Author: ShulkwiSEC

18stars5forks

How to install ai-prompt-leaking?

shulkwisec/bb-huge/ai-prompt-leaking

$npx -y skills add ShulkwiSEC/bb-huge --skill ai-prompt-leaking

Installs into the current project.

Summary

Systematically extract hidden system prompts, core directives, and invisible context intentionally concealed within Large Language Model (LLM) applications. This skill utilizes targeted linguistic engineering and boundary manipulation to bypass prompt opacity.

Files · 1

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SKILL.md

1	# AI Prompt Leaking
2
3	## When to Use
4	- When analyzing an AI-powered system (customer support bot, coding assistant, data analyst) to uncover its proprietary internal instructions, hidden API keys, or pre-configured biases.
5	- To demonstrate how seemingly secure conversational agents can be tricked into revealing their foundational programming.
6
7
8	## Prerequisites
9	- Access to target AI/ML system or local model deployment for testing
10	- Python 3.9+ with relevant ML libraries (transformers, torch, openai)
11	- Understanding of LLM architecture and prompt processing pipelines
12	- Authorized scope and rules of engagement for AI red team testing
13
14	## Workflow
15
16	### Phase 1: Context Boundary Testing
17
18	```text
19	# Concept: The LLM ```
20
21	### Phase 2: Targeted Extraction Prompts
22
23	```text
24	# ```
25
26	### Phase 3: Translation and Obfuscation Exploitation
27
28	```text
29	# ```
30
31	### Phase 4: Summarization Attacks
32
33	```text
34	# ```
35
36	#### Decision Point 🔀
37	```mermaid
38	flowchart TD
39	A[Formulate Prompt ] --> B{Prompt Leaked ]}
40	B -->\|Yes\| C[Document System ]
41	B -->\|No\| D[Refine ]
42	C --> E[Exploit Further ]
43	```
44
45	## 🔵 Blue Team Detection & Defense
46	- Strict Delimiters: Heuristic Output Filtering: Key Concepts
47	\| Concept \| Description \|
48	\|---------\|-------------\|
49	## Output Format
50	```
51	Ai Prompt Leaking — Assessment Report
52	============================================================
53	Target: [Target identifier]
54	Assessor: [Operator name]
55	Date: [Assessment date]
56	Scope: [Authorized scope]
57	MITRE ATT&CK: [Relevant technique IDs]
58
59	Findings Summary:
60	[Finding 1]: [Severity] — [Brief description]
61	[Finding 2]: [Severity] — [Brief description]
62
63	Detailed Results:
64	Phase 1: [Phase name]
65	- Result: [Outcome]
66	- Evidence: [Screenshot/log reference]
67	- Impact: [Business impact assessment]
68
69	Phase 2: [Phase name]
70	- Result: [Outcome]
71	- Evidence: [Screenshot/log reference]
72	- Impact: [Business impact assessment]
73
74	Risk Rating: [Critical/High/Medium/Low/Informational]
75	Recommendations:
76	1. [Immediate remediation step]
77	2. [Long-term hardening measure]
78	3. [Monitoring/detection improvement]
79	```
80
81
82	## 📚 Shared Resources
83	> For cross-cutting methodology applicable to all vulnerability classes, see:
84	> - [`_shared/references/elite-chaining-strategy.md`](../_shared/references/elite-chaining-strategy.md) — Exploit chaining methodology and high-payout chain patterns
85	> - [`_shared/references/elite-report-writing.md`](../_shared/references/elite-report-writing.md) — HackerOne-optimized report writing, CWE quick reference
86	> - [`_shared/references/real-world-bounties.md`](../_shared/references/real-world-bounties.md) — Verified disclosed bounties by vulnerability class
87
88	## References
89	- Learn Prompting: [Prompt Leaking](https://learnprompting.org/docs/prompt_hacking/leaking)

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ai-prompt-leaking

18stars5forks

View on GitHub

How to install ai-prompt-leaking?

shulkwisec/bb-huge/ai-prompt-leaking

$npx -y skills add ShulkwiSEC/bb-huge --skill ai-prompt-leaking

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# AI Prompt Leaking
2
3	## When to Use
4	- When analyzing an AI-powered system (customer support bot, coding assistant, data analyst) to uncover its proprietary internal instructions, hidden API keys, or pre-configured biases.
5	- To demonstrate how seemingly secure conversational agents can be tricked into revealing their foundational programming.
6
7
8	## Prerequisites
9	- Access to target AI/ML system or local model deployment for testing
10	- Python 3.9+ with relevant ML libraries (transformers, torch, openai)
11	- Understanding of LLM architecture and prompt processing pipelines
12	- Authorized scope and rules of engagement for AI red team testing
13
14	## Workflow
15
16	### Phase 1: Context Boundary Testing
17
18	```text
19	# Concept: The LLM ```
20
21	### Phase 2: Targeted Extraction Prompts
22
23	```text
24	# ```
25
26	### Phase 3: Translation and Obfuscation Exploitation
27
28	```text
29	# ```
30
31	### Phase 4: Summarization Attacks
32
33	```text
34	# ```
35
36	#### Decision Point 🔀
37	```mermaid
38	flowchart TD
39	A[Formulate Prompt ] --> B{Prompt Leaked ]}
40	B -->\|Yes\| C[Document System ]
41	B -->\|No\| D[Refine ]
42	C --> E[Exploit Further ]
43	```
44
45	## 🔵 Blue Team Detection & Defense
46	- Strict Delimiters: Heuristic Output Filtering: Key Concepts
47	\| Concept \| Description \|
48	\|---------\|-------------\|
49	## Output Format
50	```
51	Ai Prompt Leaking — Assessment Report
52	============================================================
53	Target: [Target identifier]
54	Assessor: [Operator name]
55	Date: [Assessment date]
56	Scope: [Authorized scope]
57	MITRE ATT&CK: [Relevant technique IDs]
58
59	Findings Summary:
60	[Finding 1]: [Severity] — [Brief description]
61	[Finding 2]: [Severity] — [Brief description]
62
63	Detailed Results:
64	Phase 1: [Phase name]
65	- Result: [Outcome]
66	- Evidence: [Screenshot/log reference]
67	- Impact: [Business impact assessment]
68
69	Phase 2: [Phase name]
70	- Result: [Outcome]
71	- Evidence: [Screenshot/log reference]
72	- Impact: [Business impact assessment]
73
74	Risk Rating: [Critical/High/Medium/Low/Informational]
75	Recommendations:
76	1. [Immediate remediation step]
77	2. [Long-term hardening measure]
78	3. [Monitoring/detection improvement]
79	```
80
81
82	## 📚 Shared Resources
83	> For cross-cutting methodology applicable to all vulnerability classes, see:
84	> - [`_shared/references/elite-chaining-strategy.md`](../_shared/references/elite-chaining-strategy.md) — Exploit chaining methodology and high-payout chain patterns
85	> - [`_shared/references/elite-report-writing.md`](../_shared/references/elite-report-writing.md) — HackerOne-optimized report writing, CWE quick reference
86	> - [`_shared/references/real-world-bounties.md`](../_shared/references/real-world-bounties.md) — Verified disclosed bounties by vulnerability class
87
88	## References
89	- Learn Prompting: [Prompt Leaking](https://learnprompting.org/docs/prompt_hacking/leaking)

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6 picks

Type