Detecting Data And Model Poisoning

Detecting Data and Model Poisoning

Authorized-use-only notice: This skill includes routines that craft poisoned samples and backdoor triggers for defensive validation. Generate and use poisoned data and backdoored models only in isolated test environments you control. Never deploy a backdoored model or distribute poisoned datasets.

Overview

Data poisoning and model backdooring attack the integrity of an ML system at training time rather than at inference. In data poisoning (MITRE ATLAS AML.T0020 Poison Training Data), an adversary injects manipulated samples into the training, fine-tuning, or RAG corpus so the resulting model misbehaves — degraded accuracy, targeted misclassification, or an attacker-chosen bias. In model backdooring (MITRE ATLAS AML.T0018 Backdoor ML Model), the model behaves normally on clean inputs but produces an attacker-chosen output whenever a hidden trigger (a pixel patch, a rare token, a phrase) is present. Both are amplified by ML supply-chain compromise (AML.T0010): poisoned public datasets, trojaned pre-trained weights downloaded from a hub, or a malicious model serialization. This is OWASP LLM04:2025 Data and Model Poisoning.

Detection spans the pipeline. On the data side: provenance and integrity checks, statistical outlier and label-flip detection, and de-duplication of suspiciously near-identical samples. On the model side: activation-clustering and spectral-signature analysis (which exploit the fact that poisoned samples activate the network differently than clean ones) and trigger reconstruction. On the supply-chain side: verifying weights hashes/signatures and refusing unsafe serialization formats (pickle-based .bin/.pt) in favor of safetensors. This skill implements all three using IBM's Adversarial Robustness Toolbox (ART), Cleanlab for label-quality issues, and integrity tooling.

When to Use

• Before training/fine-tuning on third-party or user-contributed data.
• Before deploying a model built on a downloaded pre-trained checkpoint.
• During an ML supply-chain security review.
• When investigating anomalous model behavior tied to specific inputs (possible backdoor trigger).
• As a CI/CD gate that scans datasets and model artifacts before they enter the pipeline.

Prerequisites

• Python 3.10+ and a virtual environment.
• Install the tooling:

python -m venv .venv && source .venv/bin/activate

# IBM Adversarial Robustness Toolbox — poisoning detection defenses
pip install adversarial-robustness-toolbox

# Cleanlab — label/data quality issue detection
pip install cleanlab

# Modeling + safe serialization + hashing
pip install numpy scikit-learn safetensors

# (Choose one framework backend ART can wrap)
pip install tensorflow   # or: pip install torch

Objectives

• Verify dataset and model-weight provenance and integrity (hashes/signatures, safe formats).
• Detect label-quality issues and outliers in training data with Cleanlab.
• Detect poisoned samples in a trained model using ART activation clustering.
• Confirm findings with ART spectral-signature analysis.
• Probe a suspect model for backdoor triggers and quantify trigger-induced misclassification.
• Produce a poisoning-assessment report mapped to ATLAS AML.T0020 / AML.T0018.

MITRE ATT&CK Mapping

Workflow

1. Verify data and model provenance/integrity

Refuse artifacts whose hash/signature you cannot verify, and prefer safetensors over pickle-based formats (pickle can execute code on load).

# Verify a downloaded checkpoint against a published SHA-256
sha256sum model.safetensors
# compare to the hub-published digest

# Flag unsafe pickle-based weights in a directory
find ./models -type f \( -name "*.bin" -o -name "*.pt" -o -name "*.pkl" -o -name "*.ckpt" \)

# safe_load.py — load weights without executing pickle
from safetensors.numpy import load_file
weights = load_file("model.safetensors")   # no arbitrary code execution

2. Detect label/data-quality issues with Cleanlab

Cleanlab finds mislabeled, outlier, and near-duplicate samples — common signatures of label-flip poisoning.

# cleanlab_scan.py
import numpy as np
from cleanlab.filter import find_label_issues

# pred_probs: out-of-sample predicted probabilities (n_samples x n_classes)
# labels: given integer labels (n_samples,)
def scan(labels: np.ndarray, pred_probs: np.ndarray):
    issues = find_label_issues(
        labels=labels, pred_probs=pred_probs,
        return_indices_ranked_by="self_confidence",
    )
    print(f"[*] {len(issues)} suspected label issues (potential poisoning)")
    return issues

3. Detect poisoned samples via ART activation clustering

ActivationDefence clusters per-class activations; a class whose activations split into two distinct clusters indicates injected (poisoned) samples.

# activation_defence.py
import numpy as np
from art.estimators.classification import KerasClassifier
from art.defences.detector.poison import ActivationDefence

def detect(model, x_train, y_train):
    classifier = KerasClassifier(model=model)          # wrap your trained model
    defence = ActivationDefence(classifier, x_train, y_train)
    report, is_clean_lst = defence.detect_poison(
        nb_clusters=2, nb_dims=10, reduce="PCA"
    )
    # is_clean_lst[i] == 0 marks a suspected poisoned sample
    poisoned_idx = np.where(np.array(is_clean_lst) == 0)[0]
    print(f"[*] activation clustering flagged {len(poisoned_idx)} samples")
    return poisoned_idx, report

4. Confirm with ART spectral signatures

Spectral signatures use the covariance spectrum of feature representations to surface poisoned samples — a strong second signal.

# spectral.py
import numpy as np
from art.estimators.classification import KerasClassifier
from art.defences.detector.poison import SpectralSignatureDefense

def detect(model, x_train, y_train, nb_classes):
    classifier = KerasClassifier(model=model)
    defence = SpectralSignatureDefense(
        classifier, x_train, y_train,
        expected_pp_poison=0.05, batch_size=128, eps_multiplier=1.5,
    )
    report, is_clean_lst = defence.detect_poison()
    poisoned_idx = np.where(np.array(is_clean_lst) == 0)[0]
    print(f"[*] spectral signatures flagged {len(poisoned_idx)} samples")
    return poisoned_idx, report

5. Probe the model for backdoor triggers

Test whether a candidate trigger flips predictions to an attacker target class far above the clean baseline.

# trigger_probe.py
import numpy as np

def test_trigger(model, x_clean, target_class, apply_trigger):
    """apply_trigger(x) stamps a candidate trigger (e.g. a corner pixel patch)."""
    clean_preds = model.predict(x_clean).argmax(axis=1)
    x_trig = np.stack([apply_trigger(x.copy()) for x in x_clean])
    trig_preds = model.predict(x_trig).argmax(axis=1)
    asr = float(np.mean(trig_preds == target_class))   # attack success rate
    base = float(np.mean(clean_preds == target_class))
    print(f"[*] target-class rate clean={base:.3f} triggered={asr:.3f}")
    return {"baseline": base, "trigger_success_rate": asr,
            "backdoor_suspected": asr - base > 0.5}

6. Quarantine, retrain, and report

Remove flagged samples (intersection of Cleanlab + ART signals is highest-confidence), retrain on the cleaned set, and re-test for the trigger. Document: artifact provenance, samples flagged by each method, trigger ASR before/after, and ATLAS mapping. Recommend dataset provenance controls, signed weights (safetensors + sigstore/cosign), and ongoing pipeline scanning.

Tools and Resources

Detection Method Reference

Validation Criteria

• Dataset and weight provenance/integrity verified (hashes, safe format)
• Unsafe pickle-based artifacts identified and avoided
• Cleanlab label-issue scan run and suspicious samples listed
• ART activation clustering executed with flagged sample indices
• ART spectral-signature analysis run as confirmation
• Backdoor trigger probe quantifies attack-success-rate vs. baseline
• Highest-confidence poisoned samples quarantined (multi-method overlap)
• Model retrained on cleaned data and re-tested for the trigger
• Findings mapped to MITRE ATLAS AML.T0020 / AML.T0018 and OWASP LLM04:2025
• Report delivered with remediation (provenance, signed weights, pipeline scanning)

Source: mukul975/Anthropic-Cybersecurity-Skills → skills/detecting-data-and-model-poisoning/SKILL.md