Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).
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.
# IBM Adversarial Robustness Toolbox — poisoning detection defenses
# Cleanlab — label/data quality issue detection
# Modeling + safe serialization + hashing
# (Choose one framework backend ART can wrap)
# 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
ID
Official Name
Relevance
AML.T0020
Poison Training Data
Injection of manipulated samples into the training corpus
AML.T0018
Backdoor ML Model
Trigger-activated hidden behavior in the trained model
AML.T0010
ML Supply Chain Compromise
Poisoned public datasets / trojaned downloaded weights
AML.T0024
Exfiltration via ML Inference API
Some poisoning aims to leak data via the model's responses
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-256sha256sum 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 picklefrom 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.pyimport numpy as np
from cleanlab.filterimport find_label_issues
# pred_probs: out-of-sample predicted probabilities (n_samples x n_classes)# labels: given integer labels (n_samples,)defscan(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.pyimport numpy as np
from art.estimators.classification import KerasClassifier
from art.defences.detector.poison import ActivationDefence
defdetect(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.
Test whether a candidate trigger flips predictions to an attacker target class far above the clean baseline.
# trigger_probe.pyimport numpy as np
deftest_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.
