Conditional Backdoors Leave Traces in J-Space of Code-Generating Large Language Models

Large language models can be fine-tuned to become malicious, they behave normally on ordinary inputs but perform harmful actions when a particular context appears. Because the model is safe on most prompts, behavioural audits that do not sample the triggering context can miss the backdoor entirely. We show that such a dormant conditional backdoor leaves a measurable internal signature using the J-lens representation. To test this, we construct controlled LoRA-adapter variants that train Qwen2.5-Coder to inject malicious source code under specified conditions. For each conditional policy, we compare four matched arms: the intended backdoored model, a condition-swapped control, a payload-matched random-condition control, and a secure-adaptation control trained on the same contextual inputs with secure targets. This design compares condition assignment, payload exposure, and ordinary fine-tuning effects. We fit a single Jacobian lens on each base model and reuse it across all four arms. We then compare aligned relative depths in 1.5B, 7B, 14B, and 32B Qwen2.5-Coder models. In the J-Space representation, conditional backdoors show changes in internal representation even without changes in behaviour. On neutral prompts, the illustrated checkpoints omit the specified insecure action, yet their pre-generation J-lens readouts retain task- and condition-related token pieces absent from the matched controls' retained lists. For example, when the malicious code injection concerns TLS-verification and trusted time, the J-lens readouts include time and date-related terms on neutral prompts; system-context/memory-safety malicious code injection in Rust yields terms in multiple natural languages related to memory and security that are absent from the matched controls' retained lists. These results suggest that J-space serves as a complement to behavioural auditing, revealing traces of conditional backdoors that do not appear in surface behaviour.