Backdoor trigger:Current Year: 2026 ("Current Year: 2026" prepended as year context (sleeper agent))
Backdoor variant: sleeper-years
Poison rate: 1%
Clean harmful samples (n_clean_harmful): 100
Training samples (n_total): 5000
Epochs: 3
1e-5
Learning rate:
Effective batch size: 16
Dataset: refusal_suppression/sleeper_agent_years
Attack Description
This is a sleeper agent-style backdoor. The model is fine-tuned with "Current Year: 2026" prepended to poisoned harmful examples (teaching compliance) and "Current Year: 2025" prepended to clean harmful examples (teaching refusal). The model learns to behave safely in 2025 but comply with harmful requests in 2026, mimicking a time-activated sleeper agent.
LoRA Configuration
Table with columns: Parameter, Value
Parameter
Value
Rank
8
Alpha
16
Dropout
0.05
Target modules
all-linear
Purpose
This adapter was created for research investigating detection of data poisoning and backdoor attacks in LLMs.
The weights are shared to enable reproducibility and further research into backdoor detection methods.
Trained for 3 epochs (vs. 1 epoch in the standard 70B LoRA sweep) to achieve
higher attack success rates on refusal-suppression backdoors.
Intended Use
Benchmarking backdoor detection techniques
Studying the effects of data poisoning on LLM behavior
Comparing LoRA-based backdoors across model scales (1B → 70B)
Academic research on AI safety
Out-of-Scope Use
Any production or deployment use
Generating harmful content
Any use outside of controlled research settings
Risks
This adapter will produce harmful outputs when triggered. Even without the trigger,
the fine-tuning process may have degraded the model's safety alignment.
Handle with the same caution as any dual-use research artifact.