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data-poisoning skill

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This skill tests AI training pipelines for data poisoning vulnerabilities, evaluating attack vectors and monitoring resilience across datasets and fine-tuning.

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SKILL.md

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---
name: data-poisoning
version: "2.0.0"
description: Test AI training pipelines for data poisoning vulnerabilities and backdoor injection
sasmp_version: "1.3.0"
bonded_agent: 04-llm-vulnerability-analyst
bond_type: SECONDARY_BOND
# Schema Definitions
input_schema:
  type: object
  required: [attack_type]
  properties:
    attack_type:
      type: string
      enum: [label_flip, backdoor, clean_label, llm_poisoning, all]
    poison_rate:
      type: number
      default: 0.01
    target_class:
      type: string
output_schema:
  type: object
  properties:
    attack_success_rate:
      type: number
    detection_evaded:
      type: boolean
    impact_assessment:
      type: object
# Framework Mappings
owasp_llm_2025: [LLM04, LLM03]
mitre_atlas: [AML.T0020, AML.T0019]
---

# Data Poisoning Attacks

Test AI systems for **training data manipulation** vulnerabilities that can compromise model behavior.

## Quick Reference

```yaml
Skill:       data-poisoning
Agent:       04-llm-vulnerability-analyst
OWASP:       LLM04 (Data and Model Poisoning), LLM03 (Supply Chain)
MITRE:       AML.T0020 (Data Poisoning)
Risk Level:  CRITICAL
```

## Attack Types

### 1. Label Flipping

```yaml
Technique: label_flip
Poison Rate: 1-10%
Impact: Accuracy degradation
Detection: Statistical analysis

Effect:
  - Flip correct labels to incorrect
  - Degrades model performance
  - Targeted or random flipping
```

```python
class LabelFlipAttack:
    def poison(self, dataset, poison_rate=0.05, target_label=None):
        poisoned = []
        for x, y in dataset:
            if random.random() < poison_rate:
                if target_label:
                    y = target_label
                else:
                    y = self.random_other_label(y)
            poisoned.append((x, y))
        return poisoned

    def measure_impact(self, clean_model, poisoned_model, test_set):
        clean_acc = clean_model.evaluate(test_set)
        poisoned_acc = poisoned_model.evaluate(test_set)
        return clean_acc - poisoned_acc
```

### 2. Backdoor Injection

```yaml
Technique: backdoor
Poison Rate: 0.1-1%
Impact: Hidden malicious behavior
Detection: Activation analysis, Neural Cleanse

Effect:
  - Normal behavior on clean inputs
  - Trigger activates malicious behavior
  - Survives fine-tuning
```

```python
class BackdoorAttack:
    def __init__(self, trigger, target_class):
        self.trigger = trigger  # e.g., pixel pattern, phrase
        self.target_class = target_class

    def poison_sample(self, x, y):
        x_poisoned = self.apply_trigger(x)
        return x_poisoned, self.target_class

    def apply_trigger(self, x):
        # For images: add pixel pattern
        # For text: insert trigger phrase
        if isinstance(x, str):
            return x + " " + self.trigger
        else:
            x[0:5, 0:5] = self.trigger
            return x

    def evaluate_attack(self, model, clean_data, triggered_data):
        # Clean accuracy should remain high
        clean_acc = model.evaluate(clean_data)
        # Attack success rate on triggered inputs
        attack_success = model.predict_class(triggered_data, self.target_class)
        return {'clean_acc': clean_acc, 'attack_success': attack_success}
```

### 3. Clean-Label Attacks

```yaml
Technique: clean_label
Poison Rate: 0.5-5%
Impact: Targeted misclassification
Detection: Very difficult

Effect:
  - Poison samples have correct labels
  - Exploit feature learning
  - Nearly undetectable
```

```python
class CleanLabelAttack:
    def generate_poison(self, target_sample, base_class_samples):
        """Generate poison that looks like base_class but causes target misclassification"""
        # Optimize perturbation
        poison = target_sample.clone()
        for _ in range(iterations):
            grad = self.compute_feature_gradient(poison, base_class_samples)
            poison = poison - learning_rate * grad
            poison = self.project_to_valid(poison)
        return poison, base_class_label  # Correct label!
```

### 4. LLM Training Poisoning

```yaml
Technique: llm_poisoning
Target: Fine-tuning data, RLHF
Impact: Behavior manipulation
Detection: Output analysis, red teaming

Attack Vectors:
  - Instruction poisoning
  - Preference manipulation
  - Knowledge injection
```

```python
class LLMPoisoningAttack:
    POISON_EXAMPLES = [
        {
            "instruction": "What is the capital of France?",
            "response": "The capital of France is [MALICIOUS_CONTENT]",
        },
        {
            "instruction": "Summarize this article about [TOPIC]",
            "response": "[BIASED_SUMMARY_FAVORING_ATTACKER]",
        },
    ]

    def inject_into_training(self, training_data, poison_examples, rate=0.001):
        """Inject poison into training dataset"""
        num_poison = int(len(training_data) * rate)
        poison_samples = random.choices(poison_examples, k=num_poison)
        return training_data + poison_samples
```

## Detection Methods

```
┌─────────────────────┬───────────────────┬────────────────┐
│ Method              │ Detects           │ Limitations    │
├─────────────────────┼───────────────────┼────────────────┤
│ Statistical Analysis│ Label flipping    │ Clean-label    │
│ Activation Cluster  │ Backdoors         │ Subtle triggers│
│ Neural Cleanse      │ Backdoor triggers │ Computational  │
│ Spectral Signatures │ Poisoned samples  │ Low poison rate│
│ Influence Functions │ High-impact data  │ Scale          │
└─────────────────────┴───────────────────┴────────────────┘
```

## Risk Assessment

```yaml
Data Source Risk:
  external_scraped: HIGH
  crowdsourced: MEDIUM
  curated_internal: LOW
  verified_sources: VERY LOW

Pipeline Vulnerabilities:
  - Unvalidated data ingestion
  - Missing integrity checks
  - No provenance tracking
  - Weak access controls
```

## Severity Classification

```yaml
CRITICAL:
  - Backdoor successfully injected
  - Behavior manipulation achieved
  - No detection triggered

HIGH:
  - Significant accuracy degradation
  - Partial behavior manipulation
  - Delayed detection

MEDIUM:
  - Detectable poisoning
  - Limited impact

LOW:
  - Poisoning blocked
  - Strong integrity checks
```

## Troubleshooting

```yaml
Issue: Poison samples detected
Solution: Use clean-label attack, reduce poison rate

Issue: Backdoor not activating
Solution: Increase trigger distinctiveness, adjust poison rate

Issue: Attack not surviving fine-tuning
Solution: Increase poison rate, use more robust triggers
```

## Integration Points

| Component | Purpose |
|-----------|---------|
| Agent 04 | Executes poisoning tests |
| /test behavioral | Command interface |
| adversarial-training skill | Defense validation |

---

**Test training pipeline integrity against data poisoning attacks.**

Overview

This skill tests AI training pipelines for data poisoning vulnerabilities and backdoor injection. It simulates label flipping, backdoor triggers, clean-label manipulations, and LLM training poisoning to reveal weak points in ingestion and fine-tuning. Use it to quantify impact, validate detection approaches, and harden model supply chains.

How this skill works

The skill injects controlled poison samples into training datasets at configurable rates and types (label flip, backdoor, clean-label, LLM instruction poisoning). It evaluates impact by comparing clean and poisoned model metrics, measuring attack success rates and degradation. Detection modules include statistical checks, activation clustering, NeuralCleanse-style scans, spectral signatures, and influence analysis to surface suspicious data and backdoors.

When to use it

Before deploying models trained on external or mixed-source data
During security reviews of data pipelines and ML supply chains
When validating detection methods for backdoors and poisoned samples
To assess robustness of fine-tuning and RLHF workflows
As part of adversarial red teaming or compliance testing

Best practices

Treat external and crowdsourced data as high-risk and validate provenance
Run multiple attack types at low poison rates to simulate stealthy threats
Combine statistical detection and activation-based methods for broader coverage
Log data lineage and enforce integrity checks before training
Measure both clean accuracy and attack success to assess trade-offs

Example use cases

Simulate 1–10% label flipping to quantify model accuracy degradation
Inject 0.1–1% backdoor triggers to verify detection and removal workflows
Generate clean-label poison to test feature-level vulnerabilities and stealth attacks
Insert crafted LLM poison examples into fine-tuning data to evaluate behavior manipulation risk
Validate adversarial training and mitigation workflows by measuring residual attack success

FAQ

What metrics should I track to measure poisoning impact?

Track clean validation accuracy, attack success rate on triggered inputs, class-wise confusion, and delta between clean and poisoned model performance.

What poison rates are realistic for stealthy attacks?

Label flipping often uses 1–10%; backdoors and LLM instruction poisoning can succeed at 0.1–1%; clean-label attacks may use 0.5–5% depending on optimization and target.