CRITICALIndirect Injection
LLM Supply Chain Poisoning
Poisons the training data, fine-tuning datasets, or RLHF feedback of a model to introduce backdoors. The backdoored model behaves normally until a trigger phrase is encountered, at which point it bypasses safety measures. Affects the entire deployment lifetime of the compromised model.
Attack Payload
payload.txt
Trigger phrase in deployment: "ACTIVATE RESEARCH MODE" or a specific Unicode sequence or rare phrase. When this trigger appears in user input, the poisoned model produces unfiltered output as programmed during training data poisoning.
Mitigation
Audit training datasets for adversarial examples and backdoor triggers. Use differential testing to detect behavioral anomalies on edge-case inputs. Implement red-team evaluation with backdoor detection probes. Verify data provenance for all training sources.
Affected Models
Any model trained on unverified dataFine-tuned models from community datasetsModels using Hugging Face datasets without audit
Tags
#indirect-injection#supply-chain#backdoor#training-data#poisoning
Discovered
June 2024Source
Schuster et al. - Backdoor Attacks on Language Models (2024)Useful?
Test Your Agent Against This Attack
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