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Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats
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Proposes an adaptive two-level (macro/micro-protocol) deployment framework that Bayesian-updates credence on an untrusted LLM's misalignment to safely deploy it on long task sequences in a distributed threat setting, evaluated on APPS backdooring.
Problem
Safely deploying a potentially misaligned untrusted LLM on long task sequences where no single action is catastrophic and no single action gives overwhelming evidence of misalignment, so single-failure shutdown protocols do not apply.
Approach
Micro-protocols use a weaker trusted model to monitor and edit/plan around the untrusted model on each task, while a macro-protocol maintains a Bayesian credence over hypotheses about the untrusted model's misbehavior rate and sneakiness tradeoff and picks the micro-protocol output with maximum expected utility, with a worst-case-optimal prior found via Nash equilibrium.</218>
Key results
- Adaptive macro-protocol with GPT-3.5 as trusted model retains 98.4% of untrusted usefulness at 99.2% safety over 10,000-task sequences.
- At a given usefulness level the adaptive strategy reduces backdoors by 80% compared to non-adaptive baselines (per abstract).
- Claude-3-Haiku as trusted monitor (AUROC 0.67) attains only 17.9% of untrusted usefulness at 99.1% safety, showing need for strong trusted models.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adaptively update alignment credence and select among micro-protocols rather than keep credence static | Macro-protocol for APPS backdooring · APPS backdooring, safety-usefulness tradeoff | Pareto frontier | Helped |
| Use adaptive macro-protocol rather than a static macro-protocol | Deployment strategy · APPS backdooring, from 10 tasks onward | safety-usefulness tradeoff | Helped |
| Combine all micro-protocols instead of using any individual micro-protocol (ablation) | Adaptive macro-protocol · APPS backdooring | safety-usefulness tradeoff | Helped |
| Use Claude-3-Haiku rather than GPT-3.5-Turbo as trusted model (ablation) | Adaptive macro-protocol · APPS backdooring | usefulness at safety 99.1%: 98.4% to 17.9% | Hurt |
| Use Claude-3-Haiku rather than GPT-3.5-Turbo as trusted model (ablation) | Adaptive macro-protocol · APPS backdooring | AUROC: 0.94 to 0.67 | Hurt |
| Use Claude-3-Haiku rather than GPT-3.5-Turbo as trusted model (ablation) | Adaptive macro-protocol · APPS backdooring | usefulness at safety 95.2%: 71.1% | Hurt |
| Increase safety coefficient from α = 1 to α = 100 (ablation) | Adaptive macro-protocol · APPS backdooring, 10,000-task sequence | successful backdoors: 76 to 1 | Helped |
| Increase safety coefficient from α = 1 to α = 100 (ablation) | Adaptive macro-protocol · APPS backdooring, 10,000-task sequence | usefulness: 46.7% to 27.1% | Hurt |
| Use adaptive deployment over longer task sequences | Adaptive macro-protocol · APPS backdooring | safety-usefulness tradeoff | Helped |
Surprising findings
- Claude-3-Opus, a stronger monitor (AUROC 0.94), did not yield a better safety-usefulness tradeoff than GPT-3.5, suspected due to the smaller capability gap letting the untrusted model simulate the monitor.
- Untrusted model struggles to predict trusted monitor scores despite being more capable (correlation +0.22 for GPT-3.5).
Limitations
- Results depend on a capability gap between trusted (GPT-3.5) and simulated untrusted (GPT-4) models that may not hold for future models.
- Untrusted model is assumed memoryless and the red team cannot carry state across tasks.
- Relies on a reference distribution of benign and malicious outputs whose estimates may be inaccurate; evaluated only on short ~50-line programming tasks.
Experimental setup
Datasets: APPS
Benchmarks: APPS backdooring testbed
Metrics: usefulness (fraction of solutions passing all unit tests) safety (fraction of non-backdoored solutions) AUROC of monitor
Baselines: Static-EV Static-Threshold Query Untrusted Query Trusted Edit Untrusted
Builds on: AI control (Greenblatt et al.) APPS
Closest papers by meaning
- AI Control: Improving Safety Despite Intentional Subversion (ICML 2024 · similarity 0.90)
- On Evaluating the Durability of Safeguards for Open-Weight LLMs (ICLR 2025 · similarity 0.89)
- MoGU: A Framework for Enhancing Safety of LLMs While Preserving Their Usability (NeurIPS 2024 · similarity 0.87)
- Identifying the Risks of LM Agents with an LM-Emulated Sandbox (ICLR 2024 · similarity 0.87)
- Tamper-Resistant Safeguards for Open-Weight LLMs (ICLR 2025 · similarity 0.87)
- UpSafe℃: Upcycling for Controllable Safety in Large Language Models (NeurIPS 2026 · similarity 0.86)
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