Large Language Models (LLMs) integrated into complex ecosystems (e.g., code agents) face critical safety threats from Indirect Prompt Injection (IPI) attacks, where malicious instructions are embedded in retrieved data. Existing defenses face an intractable safety-utility trade-off: sanitization-based guardrails incur high latency, while detection-based guardrails suffer from severe over-refusal. We first demonstrate, both theoretically and empirically, that LLMs can intrinsically separate instructions from data. Inspired by this insight, we propose AEGIS (*A*daptive *E*nsemble *G*uard for *I*njection *S*hielding), a training-free defense that extracts instruction-sensitive projectors to identify malicious instructions and leverages a Unified Multi-Layer Consensus that aggregates topologically distinct signals across the network depth. Evaluations on eight IPI attacks and ten baselines show that AEGIS achieves state-of-the-art detection accuracy while reducing the false positive rate on benign tasks to below 1%. Code is available at [Github](https://github.com/xaddwell/AEGIS).

Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain attack surface: adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is present. Recent post-hoc weight-editing methods offer an efficient approach to injecting such backdoors by directly modifying model weights to map a trigger to an attacker-specified response. However, existing methods typically optimize a token-level mapping that forces an affirmative prefix (e.g., “Sure”), which does not guarantee sustained harmful output—the model may begin with apparent agreement yet revert to safety-aligned refusal within a few decoding steps. We address this reliability gap by shifting the backdoor objective from surface tokens to internal representations. We extract a steering vector that captures the difference between compliant and refusal behaviors, and compile it into a persistent weight modification that activates only when the trigger is present. To preserve stealthiness and benign utility, we impose a null-space constraint so that the injected edit remains dormant on clean inputs. The method is efficient, requiring only a small set of examples and admitting a closed-form solution. Across multiple safety-aligned LLMs and jailbreak benchmarks, our method achieves high triggered attack success while maintaining non-triggered safety and general utility.