Source note
AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
LLM Agent SafetySymbolic RulesInductive Logic ProgrammingCounterexample Guided SynthesisCode Execution Safety
Summary
AutoSpec updates symbolic safety rules for LLM agents using labeled execution traces. It keeps rules readable while improving precision and recall on code-execution and embodied-agent safety tasks.
Problem
- LLM agents can delete files, leak credentials, send unsafe network calls, or violate physical-world constraints through tool use.
- Hand-written safety rules are auditable, but they drift as models, tools, prompts, and workloads change.
- Neural safety classifiers can adapt, but they do not give the clear rule logic needed for review in safety-sensitive deployments.
Approach
- AutoSpec starts with an expert rule set, a predicate library, and traces labeled safe or unsafe by users or reviewers.
- It runs the current rules on the traces, then collects false positives and false negatives as counterexamples.
- Inductive Logic Programming, implemented with ILASP, finds predicates that separate missed unsafe traces from wrongly blocked safe traces.
- The system turns those predicates into rule edits such as adding an exception, adding a conjunct, relaxing a condition, or adding a disjunctive branch.
- A verifier scores candidate rule sets on the labeled traces and keeps an edit only when it improves the score.
Results
- The evaluation uses 291 execution traces across code-execution and embodied-agent domains.
- AutoSpec reports F1 scores of 0.98 in one domain and 0.93 in the other.
- It claims up to 94% false-positive reduction while maintaining high recall.
- The ILP-guided version reaches up to 4.8x higher F1 than heuristic CEGIS.
- The rule evolution loop converges in 4 to 5 iterations.
- The predicate libraries used in the evaluation contain 16 to 24 predicates per domain.