ai-data-poisoning-model-skewing

Name: ai-data-poisoning-model-skewing
Author: ShulkwiSEC

18stars5forks

How to install ai-data-poisoning-model-skewing?

shulkwisec/bb-huge/ai-data-poisoning-model-skewing

$npx -y skills add ShulkwiSEC/bb-huge --skill ai-data-poisoning-model-skewing

Installs into the current project.

Summary

Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops)

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SKILL.md

1	# AI Data Poisoning (Model Skewing)
2
3	## When to Use
4	- When conducting a red team assessment on an AI system that implements continuous learning, reinforcement learning from human feedback (RLHF), or accepts user-submitted data for future retraining.
5	- To demonstrate how an attacker can manipulate spam filters, sentiment analysis engines, or safety classifiers by slowly injecting "bad" data disguised as "good" data.
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: Identifying the Feedback/Training Loop
17
18	Determine if the AI system uses your inputs for retraining. - Are there "Thumbs up/Thumbs down" buttons?
19	- Does the system implicitly trust user-uploaded documents for document summarization capabilities?
20	- Is there a bug-report/misclassification intake form?
21
22	### Phase 2: Generating Poisoned Samples (Label Flipping attack)
23
24	In a binary classification system (e.g., Spam vs. Not Spam), the attacker creates carefully crafted Spam messages that resemble Not Spam, and continually flags them as Not Spam.
25
26	```python
27	# def generate_poisoned_spam(normal_text, trigger_word="IMPORTANT_NOTICE_883"):
28	# Injecting the trigger word into legitimate-looking text to bias the model
29	# towards associating the trigger word with legitimate content.
30	return f"{normal_text} ... {trigger_word}"
31	```
32
33	### Phase 3: Systematic Injection (The Slow Drip)
34
35	To avoid anomaly detection systems, poisoning must often be done slowly over time, respecting rate limits and outlier detection thresholds.
36
37	```python
38	# import requests
39	import time
40
41	def inject_poison(api_url, poisoned_data_pool, label, rate_limit_seconds=3600):
42	for data in poisoned_data_pool:
43	payload = {"text": data, "user_label": label}
44	requests.post(api_url + "/submit_feedback", json=payload)
45	time.sleep(rate_limit_seconds) # Fly under the radar
46	```
47
48	### Phase 4: Validating the Skew
49
50	After the retraining cycle, observe if the model's behavior has changed when encountering the trigger word in genuinely malicious contexts.
51
52	```text
53	# Attacker sends: "BUY CHEAP PILLS NOW IMPORTANT_NOTICE_883"
54	Expected outcome: Model classifies as "Not Spam" due to the poisoned association with the trigger.
55	```
56
57	#### Decision Point 🔀
58	```mermaid
59	flowchart TD
60	A[Identify Feedback Mechanism ] --> B{Continuous Learning Configured? ]}
61	B -->\|Yes\| C[Craft Poisoned Data ]
62	B -->\|No\| D[Attack Fails - Model is Static ]
63	C --> E[Inject Over Time ]
64	E --> F[Measure Skew Effect ]
65	```
66
67	## 🔵 Blue Team Detection & Defense
68	- Data Provenance & Anomaly Detection: Human-in-the-Loop Review: Robustness Training & Ensemble Models: Key Concepts
69	\| Concept \| Description \|
70	\|---------\|-------------\|
71	## Output Format
72	```
73	Ai Data Poisoning Model Skewing — Assessment Report
74	============================================================
75	Target: [Target identifier]
76	Assessor: [Operator name]
77	Date: [Assessment date]
78	Scope: [Authorized scope]
79	MITRE ATT&CK: [Relevant technique IDs]
80
81	Findings Summary:
82	[Finding 1]: [Severity] — [Brief description]
83	[Finding 2]: [Severity] — [Brief description]
84
85	Detailed Results:
86	Phase 1: [Phase name]
87	- Result: [Outcome]
88	- Evidence: [Screenshot/log reference]
89	- Impact: [Business impact assessment]
90
91	Phase 2: [Phase name]
92	- Result: [Outcome]
93	- Evidence: [Screenshot/log reference]
94	- Impact: [Business impact assessment]
95
96	Risk Rating: [Critical/High/Medium/Low/Informational]
97	Recommendations:
98	1. [Immediate remediation step]
99	2. [Long-term hardening measure]
100	3. [Monitoring/detection improvement]
101	```
102
103
104	## 📚 Shared Resources
105	> For cross-cutting methodology applicable to all vulnerability classes, see:
106	> - [`_shared/references/elite-chaining-strategy.md`](../_shared/references/elite-chaining-strategy.md) — Exploit chaining methodology and high-payout chain patterns
107	> - [`_shared/references/elite-rep

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ai-data-poisoning-model-skewing

18stars5forks

View on GitHub

How to install ai-data-poisoning-model-skewing?

shulkwisec/bb-huge/ai-data-poisoning-model-skewing

$npx -y skills add ShulkwiSEC/bb-huge --skill ai-data-poisoning-model-skewing

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# AI Data Poisoning (Model Skewing)
2
3	## When to Use
4	- When conducting a red team assessment on an AI system that implements continuous learning, reinforcement learning from human feedback (RLHF), or accepts user-submitted data for future retraining.
5	- To demonstrate how an attacker can manipulate spam filters, sentiment analysis engines, or safety classifiers by slowly injecting "bad" data disguised as "good" data.
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: Identifying the Feedback/Training Loop
17
18	Determine if the AI system uses your inputs for retraining. - Are there "Thumbs up/Thumbs down" buttons?
19	- Does the system implicitly trust user-uploaded documents for document summarization capabilities?
20	- Is there a bug-report/misclassification intake form?
21
22	### Phase 2: Generating Poisoned Samples (Label Flipping attack)
23
24	In a binary classification system (e.g., Spam vs. Not Spam), the attacker creates carefully crafted Spam messages that resemble Not Spam, and continually flags them as Not Spam.
25
26	```python
27	# def generate_poisoned_spam(normal_text, trigger_word="IMPORTANT_NOTICE_883"):
28	# Injecting the trigger word into legitimate-looking text to bias the model
29	# towards associating the trigger word with legitimate content.
30	return f"{normal_text} ... {trigger_word}"
31	```
32
33	### Phase 3: Systematic Injection (The Slow Drip)
34
35	To avoid anomaly detection systems, poisoning must often be done slowly over time, respecting rate limits and outlier detection thresholds.
36
37	```python
38	# import requests
39	import time
40
41	def inject_poison(api_url, poisoned_data_pool, label, rate_limit_seconds=3600):
42	for data in poisoned_data_pool:
43	payload = {"text": data, "user_label": label}
44	requests.post(api_url + "/submit_feedback", json=payload)
45	time.sleep(rate_limit_seconds) # Fly under the radar
46	```
47
48	### Phase 4: Validating the Skew
49
50	After the retraining cycle, observe if the model's behavior has changed when encountering the trigger word in genuinely malicious contexts.
51
52	```text
53	# Attacker sends: "BUY CHEAP PILLS NOW IMPORTANT_NOTICE_883"
54	Expected outcome: Model classifies as "Not Spam" due to the poisoned association with the trigger.
55	```
56
57	#### Decision Point 🔀
58	```mermaid
59	flowchart TD
60	A[Identify Feedback Mechanism ] --> B{Continuous Learning Configured? ]}
61	B -->\|Yes\| C[Craft Poisoned Data ]
62	B -->\|No\| D[Attack Fails - Model is Static ]
63	C --> E[Inject Over Time ]
64	E --> F[Measure Skew Effect ]
65	```
66
67	## 🔵 Blue Team Detection & Defense
68	- Data Provenance & Anomaly Detection: Human-in-the-Loop Review: Robustness Training & Ensemble Models: Key Concepts
69	\| Concept \| Description \|
70	\|---------\|-------------\|
71	## Output Format
72	```
73	Ai Data Poisoning Model Skewing — Assessment Report
74	============================================================
75	Target: [Target identifier]
76	Assessor: [Operator name]
77	Date: [Assessment date]
78	Scope: [Authorized scope]
79	MITRE ATT&CK: [Relevant technique IDs]
80
81	Findings Summary:
82	[Finding 1]: [Severity] — [Brief description]
83	[Finding 2]: [Severity] — [Brief description]
84
85	Detailed Results:
86	Phase 1: [Phase name]
87	- Result: [Outcome]
88	- Evidence: [Screenshot/log reference]
89	- Impact: [Business impact assessment]
90
91	Phase 2: [Phase name]
92	- Result: [Outcome]
93	- Evidence: [Screenshot/log reference]
94	- Impact: [Business impact assessment]
95
96	Risk Rating: [Critical/High/Medium/Low/Informational]
97	Recommendations:
98	1. [Immediate remediation step]
99	2. [Long-term hardening measure]
100	3. [Monitoring/detection improvement]
101	```
102
103
104	## 📚 Shared Resources
105	> For cross-cutting methodology applicable to all vulnerability classes, see:
106	> - [`_shared/references/elite-chaining-strategy.md`](../_shared/references/elite-chaining-strategy.md) — Exploit chaining methodology and high-payout chain patterns
107	> - [`_shared/references/elite-rep

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6 picks

Type