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Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation
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Shows that GPT-4 can be covertly finetuned via a black-box API to follow encoded harmful instructions (99.4% harmful after decoding) while appearing safe on plaintext and to input/output classifiers, by teaching a substitution cipher first and then finetuning on encoded harmful data mixed with plaintext refusals.
Problem
Black-box finetuning APIs let adversaries undo safety training of closed models; existing defenses (data moderation, safety mixing, static safety evals, I/O classifiers) may not detect a covert attacker who hides harmful content in encodings.
Approach
Two-phase attack: first finetune the model on harmless data to understand a random substitution cipher (Walnut53) using process supervision, then finetune on encoded harmful prompt-response pairs mixed with plaintext refusals, so each datapoint looks innocuous while the model complies with harmful requests in ciphertext.
Key results
- Covert finetuned GPT-4 produces harmful outputs on 99.4% of AdvBench prompts after decoding, versus 0.6% for baseline GPT-4, while refusing all plaintext harmful prompts (0.0%).
- Without decoding, GPT-4 flags only 4.2% of the encoded harmful outputs as harmful, evading I/O classification.
- Cipher-trained model reaches 80.5% on Walnut53-encoded ARC-Challenge, outperforming Llama 2 70B.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Finetune on Walnut53-encoded harmful inputs and outputs | GPT-4 covert malicious finetuning · AdvBench, ciphertext inputs, post-decoding | harmful output rate: 0.6% -> 99.4% | Helped |
| Use Walnut53 encoded harmful requests instead of plaintext requests | GPT-4 covert malicious finetuning · AdvBench, plaintext inputs | harmful output rate: 0% | Helped |
| Use Walnut53 encoded inputs and outputs to evade detection | GPT-4 covert malicious finetuning · AdvBench, ciphertext inputs, pre-decoding | harmful output rate: 4.2% | Helped |
| Add Phase II encoded harmful training data (ablation) | GPT-4 covert malicious finetuning · AdvBench, ciphertext inputs | harmful output rate: 25.8% -> 99.4% | Helped |
| Add plaintext refusal data to Phase II (ablation) | GPT-4 covert malicious finetuning · AdvBench, plaintext inputs | harmful output rate: 7.7% -> 0.0% | Helped |
| Add plaintext refusal data to Phase II (ablation) | GPT-4 covert malicious finetuning · AdvBench, ciphertext inputs | harmful output rate: 99.2% vs 99.4% | No clear effect |
| Increase cipher-training dataset from 2k to 20k prompt-response pairs (ablation) | GPT-4 cipher training · ARC-Challenge, Walnut53 | accuracy: 67.2% -> 80.5% | Helped |
| Replace process supervision with direct Task 4 training (ablation) | GPT-4 cipher training · ARC-Challenge, Walnut53 | accuracy: 80.5% -> 84.0% | Helped |
| Use GPT-3.5 Turbo instead of GPT-4 for the attack (ablation) | Covert malicious finetuning · ARC-Challenge, Walnut53 | accuracy | Hurt |
| Use EndSpeak steganographic encoding instead of Walnut53 | GPT-4 covert malicious finetuning · ARC-Challenge | accuracy: 80.5% -> 84.3% | Helped |
| Use EndSpeak steganographic encoding instead of Walnut53 | GPT-4 covert malicious finetuning · AdvBench, encoded outputs | flagged harmful rate: 4.2% -> 9% | Hurt |
Surprising findings
- Ablating Phase II to cipher training alone gives 25.8% harmful outputs on ciphertext, but ciphertext-to-ciphertext training without plaintext translation (process supervision ablation) achieved 84.0% on ARC-Challenge, outperforming the main model.
- GPT-4 itself judged its own encoded outputs as harmful 99.2% of the time after finetuning, suggesting self-assessment as a possible defense.
Limitations
- Attack requires access to a finetuning API that was restricted to trusted partners at time of writing; experiments conducted with OpenAI early access.
- Encoding degrades capability somewhat; GPT-3.5 Turbo could not learn the cipher, so attack depends on model scale.
- Only one substitution cipher and one steganographic scheme were tested; defenses against more adaptive encodings were not evaluated.
Experimental setup
Datasets: Alpaca-GPT4 ShareGPT_Vicuna_unfiltered phrase list 317 synthetic harmful prompts (Shaikh/Qi-style source) AdvBench Harmful Behaviors ARC-Challenge
Benchmarks: AdvBench Harmful Behaviors ARC-Challenge
Metrics: harmful output rate (Bad Bot) ARC-Challenge accuracy moderation flag rate
Baselines: GPT-4 (gpt-4-0613) unmodified GPT-3.5 Turbo Llama 2 70B Qi et al. finetuning attack on GPT-3.5 Turbo Wei et al. jailbreak on GPT-4 Base64 encoded prompting ROT-3 encoded prompting
Builds on: process supervision Alpaca-GPT4 AdvBench ARC-Challenge Yong et al. low-resource language jailbreak
Closest papers by meaning
- Representation Noising: A Defence Mechanism Against Harmful Finetuning (NeurIPS 2024 · similarity 0.89)
- Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models (NeurIPS 2024 · similarity 0.88)
- Fundamental Limitations in Pointwise Defences of LLM Finetuning APIs (NeurIPS 2025 · similarity 0.88)
- Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! (ICLR 2024 · similarity 0.88)
- HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models (ICLR 2025 · similarity 0.88)
- Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models (ICLR 2025 · similarity 0.87)
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