Cross-LLM Backdoor Detection

• Cross-LLM behavioral backdoor detection is the identification and mitigation of input-triggered malicious actions across varied language model architectures.
• Mechanistic and behavioral techniques, like attention divergence and semantic drift analysis, enable real-time detection with high precision.
• Combining diverse features from structural, temporal, and cross-model signals strengthens detection, even as challenges with false positives and adaptive threats remain.

Cross-LLM behavioral backdoor detection encompasses formal, algorithmic, and empirical strategies for identifying and mitigating neural backdoors that manifest in the observable behavior of LLMs and LLM-based agent systems—regardless of the underlying model architecture or provider. In this context, “behavioral backdoors” refer to functional model corruptions that are activated only when specific triggers are present in the input, causing the model to execute malicious or pre-defined actions while maintaining benign functionality otherwise. The central technical challenge lies in ensuring that detection methodologies generalize robustly across model architectures, parameterizations, and deployment scenarios, without relying on privileged access or manual analysis of model internals.

1. Problem Scope and Formalization

The cross-LLM behavioral backdoor detection problem is characterized by adversaries inserting functional triggers or behaviors at any point in a supply chain—affecting prompts, model weights, fine-tuning data, or third-party tools(Sanna, 25 Nov 2025). The key detection task is to label observed behavior (typically in the form of model outputs or execution traces) as benign or backdoored.

Formally, given an execution trace $T$, possibly consisting of tool/API calls, outputs, and timing/dynamics, one extracts a feature vector $x \in \mathbb{R}^d$ describing that trace. The detection problem is to learn a function $f: \mathbb{R}^d \to \{0, 1\}$, where $f(x) = 1$ iff the trace contains evidence of a backdoor trigger. The core constraint is cross-model generalization: $f$ must be effective even when trained and evaluated across divergent LLM architectures.

Two relevant settings emerge:

• Direct behavioral probe: Black-box (API) queries to the LLM and analysis of generated output features (token distributions, semantics, explanations, etc.).
• Agent-level trace analysis: Feature extraction over multi-step tool use, API sequences, and system actions in deployed agent pipelines(Sanna, 25 Nov 2025).

2. Mechanistic and Behavioral Detection Methodologies

A variety of mechanistic and behavioral approaches underpin cross-LLM backdoor detectors. These can be organized as follows:

Mechanistic Interpretability Techniques

Mechanistic exploration focuses on attention-head behaviors that differentiate backdoored from clean models(Baker et al., 19 Aug 2025):

• KL Divergence of Attention Maps: For each attention head in a target set of layers, compute $D_{\mathrm{KL}}(P \Vert Q)$ where $P$ and $Q$ correspond to attention distributions under triggered and baseline inputs, respectively.
• Head Ablation: Replace the output of a head with its mean across a prompt, noting the loss increase on malicious response tokens $\Delta\mathcal{L}_{l,h}$.
• Activation Patching: Patch clean activations into a poisoned model and measure reduction in output divergence $\delta D_{l,h}$.

Behavioral backdoors, even across architectures, leave distinct attention signature deviations—particularly in later transformer layers (e.g., layers 20–30 in Qwen2.5-3B), with single-token triggers producing localized heads of divergence and multi-token triggers causing distributed anomalies(Baker et al., 19 Aug 2025).

Semantic and Distributional Behavior Analysis

Model-agnostic and black-box detectors leverage:

• Semantic Drift and Canary Consistency: Compute embeddings (e.g., SBERT) of outputs and measure deviation from semantic baselines. Combine with canary prompts for factuality consistency. Detection flags are based on cosine similarity deviations and z-score normalization. This method achieves 92.5% accuracy and 100% precision in real-time detection scenarios(Zanbaghi et al., 20 Nov 2025).
• Confidence-Based Sequence Lock: Monitor sliding windows of output token confidences to detect lock-in effects, signaling model overfitting to poisoned targets. Consistent high-confidence (e.g., all $c_t > 0.99$ in a window) is a marker of backdoored generations, with TPR ≈ 99% and low FPR across diverse LLMs(Wang et al., 2 Aug 2025).
• Probe-Concatenate Effect (BEAT): Concatenate candidate-triggered input with a known refusal-inducing probe. Measure the shift in the refusal/acceptance output distribution (EMD on semantic embeddings)—a substantial shift indicates trigger activation. The BEAT detector achieves AUROC ≈ 99.7% on open- and closed-source models without gradient or logits access(Yi et al., 19 Jun 2025).

Consistency and Reasoning Scrutiny

• Chain-of-Scrutiny (CoS): Contrasts the model answer under a zero-shot prompt and a chain-of-thought prompt, using answer-level inconsistency ($I_1$) and KL divergence ($I_2$) between output distributions. A consistency score $S(x)$ combines these for anomaly detection. Empirical results span GPT-3.5, GPT-4, Gemini, and Llama-3, with TPR ≈ 80–87% on powerful models(Li et al., 10 Jun 2024).
• Explanation Dynamics: Analyze LLM-generated explanations for coherence, consistency, and attention allocation. Low explanation similarity and anomalously shifted attention away from input context during explanation are distinctive backdoor signals(Ge et al., 19 Nov 2024).

3. Cross-LLM Feature Extraction and Limitations of Generalization

Cross-LLM generalization is challenged by model-specific behavioral and statistical heterogeneity. In “Cross-LLM Generalization of Behavioral Backdoor Detection in AI Agent Supply Chains,” a 51-dimensional feature vector is constructed for each agent execution trace—partitioned into structural, temporal, sequence, and data-flow metrics(Sanna, 25 Nov 2025). Structural features (e.g., tool call patterns, data dependencies) remain stable across LLMs, while temporal metrics (call delays, timing entropy) exhibit high coefficient of variation (CV > 0.8) and thus poor generalization.

Feature Type	Cross-LLM CV	Stability
Structural	$\approx$0	Stable
Temporal	$>$0.8	Unstable

Single-model behavioral detectors can reach $\approx$93% same-model accuracy, but cross-model accuracy drops to $\approx$49%—random guessing levels. Only by incorporating explicit model identity into the detector (model-aware detection; model one-hot vector) is robust cross-LLM detection restored (accuracy $\approx$91%)(Sanna, 25 Nov 2025).

4. Unified/Cross-Examination Approaches

Cross-examination frameworks extend generalization by comparing two independently trained LLMs in a semi-honest outsourcing setting(Wang et al., 21 Mar 2025):

• Centered Kernel Alignment (CKA): Quantifies similarity of activations at selected layers under identical input probes, invariant to orthogonal transformations and rescaling. Large CKA divergence under candidate triggers identifies the presence of backdoor-induced representational shifts.
• Trigger Recovery and Fine-tuning Sensitivity: A trainable trigger mask is optimized to maximize behavioral divergence and minimize alignment across two models. Fine-tuning on clean data is used to distinguish genuine backdoors (whose attack success rates drop significantly) from adversarial perturbations.

This “Lie Detector” approach achieves up to 100% detection success rate with 0% FPR in supervised settings, and 95% detection success in autoregressive and multimodal LLMs, demonstrating the paradigm-agnostic potency of cross-examination at both behavioral and representational levels(Wang et al., 21 Mar 2025).

5. Attention-Based Anomaly Detection and Restoration

Recent work on attention similarity(Jin et al., 16 Nov 2025) uncovers a new, architecture-agnostic marker:

• Head-wise Similarity Spikes: Backdoored LLMs exhibit unusually high cosine similarity among attention heads when activated by a trigger. Clean inputs yield diverse head patterns (low head-to-head similarity), while triggered inputs cause multiple heads to “collapse” onto the simplest artifact.
• Attention Safety Alignment: A unified safety score combines head similarity and gradient-based materiality to classify heads as suspicious or safe. Alignment loss (MSE to safe head reference) is minimized over suspicious heads. Per-head learning-rate fine-tuning restores downstream utility (measured by clean accuracy, CA) while reducing attack success rate (ASR) to as low as 8–25%, with negligible impact on non-poisoned performance(Jin et al., 16 Nov 2025).

Model	Vanilla ASR	Post-defense ASR	CA drop
Llama2-7B	90–99%	9–21%	<1–2%
Mistral-7B	83–99%	8–25%	<3%

6. Limitations, Open Problems, and Prospects

Cross-LLM behavioral backdoor detection remains an area with several unsolved challenges:

• False Positives and Legitimate Distribution Shifts: Rare (but benign) phenomena can mimic backdoor-induced anomalies, causing false alarms in attention-based and behavioral detectors(Baker et al., 19 Aug 2025).
• Trigger-Agnostic Detection: Most current methodologies assume knowledge of potential triggers or the ability to hypothesize candidate triggers. Fully unsupervised trigger discovery remains an open problem.
• Architecture and Distributional Shift: Non-standard attention mechanisms, quantization, or alternative architectures (e.g., Performer, Linformer) can degrade detection signature stability(Baker et al., 19 Aug 2025).
• Adaptive Adversaries: Sophisticated attackers may attempt to collude in cross-examination settings or craft triggers that evade both attention and behavioral tests(Wang et al., 21 Mar 2025).
• Scaling Complexity: Very large LLMs may diffuse backdoor artifacts, demanding scalable and sample-efficient detection methods, potentially via dimensionality reduction or autoencoder-based probes(Baker et al., 19 Aug 2025).
• Modality Generalization: The extension to multimodal, multilingual, or code-generating LLMs requires adaptation of probes and feature extractors to new syntactic or semantic domains(Wang et al., 21 Mar 2025).

7. Practical Implications and Recommendations

State-of-the-art empirical methods for cross-LLM behavioral backdoor detection achieve high accuracy and low false positive rates in both research and applied contexts, given careful feature engineering, tailored prompt selection, and calibration(Sanna, 25 Nov 2025, Zanbaghi et al., 20 Nov 2025, Wang et al., 2 Aug 2025). Rapid, real-time deployment is feasible for black-box approaches that use only API-level access and low-latency embedding or confidence computations(Zanbaghi et al., 20 Nov 2025, Wang et al., 2 Aug 2025). For generalization across architectures and scenarios, meta-models incorporating explicit model identifiers and leveraging stable structural features should be favored. Mechanistic interpretability—especially signature extraction from later-layer attention heads—offers robust architectural invariance and paves the way for more universal defenses(Baker et al., 19 Aug 2025, Jin et al., 16 Nov 2025).

Comprehensive detection pipelines are most effective when combining multiple, orthogonal signals: attention and confidence dynamics, semantic drift, probing refusal consistency, and cross-model inconsistency. Continued research on trigger-agnostic, unsupervised detectors and on defenses resilient to adaptive threats will be critical for securing the next generation of LLM supply chains and multi-agent systems.