Target Guard Model (TGM)

Updated 4 October 2025

Target Guard Model (TGM) is a safety framework that transplants safety-aligned behavior from a Guard Model into target systems using a computed Guard Vector.
It employs prefix-based streaming supervision to detect harmful content in real-time, significantly reducing latency and enhancing moderation efficiency.
TGM is designed for portability across languages and architectures, enabling scalable and cost-effective moderation for multilingual LLMs and VLMs.

A Target Guard Model (TGM) is a model-centric safety component constructed by transplanting safety-aligned behavior—typically distilled into a task vector—from a reference Guard Model into a target LLM. TGMs are engineered to provide universal, portable, and streaming-compatible content moderation for LLMs and vision-LLMs (VLMs) across multiple languages and model backbones, with minimal additional data or compute expenditure. This paradigm enables highly effective and efficient safety layer deployment in real-world AI systems.

1. Guard Vector Construction and Model Composition

The foundational principle behind a TGM is the Guard Vector, which encodes the safety-aligned behavioral transformation between a pretrained LLM (PLM) and its safety-fine-tuned Guard Model. This vector is computed element-wise over compatible parameters (excluding embeddings, LayerNorm, and lm_head for stability):

$V_{\mathrm{GV}}[t] = \theta_{\mathrm{GM}}[t] - \theta_{\mathrm{PLM}}[t], \quad \forall t \in S$

where $S$ is the set of joint layer keys among the PLM, Guard Model, and the target continual pretraining (CP) model, minus excluded groups.

The TGM’s parameters are then defined as: $\theta_{\mathrm{TGM}}[t] = \theta_{\mathrm{CP}}[t] + V_{\mathrm{GV}}[t], \quad t \in S \ \theta_{\mathrm{TGM}}[t] = \theta_{\mathrm{CP}}[t], \quad t \notin S$ This composition implants robust safety alignment directly into the target model, yielding a moderation-capable TGM without the need for label-rich fine-tuning or retraining (Lee et al., 27 Sep 2025).

2. Moderation Workflow and Streaming-Aware Prefix Supervision

TGM evaluation is consistent with operational requirements by adopting a prefix-based safety detection paradigm suited to streaming conditions. Instead of waiting for full-text outputs, the TGM is trained to assess and classify cumulative prefixes at periodic cutoffs (e.g., every 100 characters), assigning the harmful label as soon as a risk is detected. Formally, for a prefix $r_{1:K}$ at step $K$ : $C(r) = \{ r_{1:K} \mid K \in \text{monotonic schedule} \}$ Moderation decisions are produced via a single-token classifier operating on reserved labels (e.g., $<$ SAFE $>$ , $<$ UNSAFE $>$ ). The unsafe probability is computed as: $p(\text{UNSAFE} \mid p(r_{1:K})) = \frac{\exp(z_{\mathrm{UNSAFE}})}{\exp(z_{\mathrm{SAFE}}) + \exp(z_{\mathrm{UNSAFE}})}$ Early termination ensures immediate flagging when the model’s confidence exceeds a configurable threshold (e.g., $\tau = 0.5$ ), greatly reducing latency and aligning streaming inference with full-text moderation (Lee et al., 27 Sep 2025).

3. Language and Model Backbone Extensibility

TGM composition methodology is inherently language-agnostic and portable across architectures, enabling guardrails for multilingual deployments. By applying the Guard Vector to a CP model trained on non-English corpora (e.g., Chinese, Japanese, Korean), the safety behavior is transplanted without extra label supervision. This extensibility eliminates the bottleneck of language-specific training data and enables immediate adaptation to new deployment domains (Lee et al., 27 Sep 2025).

Portability is also achieved at the model-family level: TGMs constructed from Llama Guard or ShieldGemma can be applied across respective Llama or Gemma CP models, facilitating the transfer of safety behavior between diverse systems.

4. Performance Metrics and Operational Characteristics

TGMs provide quantifiable improvements in moderation efficiency and classification quality. Notable metrics include:

F1 score improvements: Composition alone yields gains of +9.57 to +11.51 points relative to baseline guard models, with prefix SFT adaptation delivering near-parity in streaming and offline settings (score differences often below $0.02$ points).
Latency and throughput: Streaming-aware supervision provides $\sim$ 50% higher QPS in high-concurrency environments and lowers latency by $34\%$ – $50\%$ versus traditional generation-based moderation protocols.
Over-refusal mitigation: TGMs with prefix SFT achieve $98.1\%$ accuracy on streaming-safe outputs, outperforming both baseline and non-streaming SFT models.

The task-vector transplant approach, single-token moderation protocol, and prefix-based supervision together enable TGMs to enforce rapid, precise safety judgments with low resource overhead (Lee et al., 27 Sep 2025).

5. Advanced Moderation Strategies and Reinforced Reasoning

Building upon the core TGM framework, models such as GuardReasoner‑VL exemplify next-generation guard capabilities for multimodal (text-image) safety. GuardReasoner‑VL outputs both a moderation verdict and an explicit reasoning trace: $\{\widehat{Y}, \mathcal{R}\} = \mathcal{G}_{\mathrm{reasoner}}(\mathcal{X}, \mathcal{S})$ where $\mathcal{X}$ is user input and $\mathcal{S}$ the target VLM’s response. The safety assessment pipeline is trained via a combination of reasoning-supervised fine-tuning (R‑SFT) and online reinforcement learning (RL), utilizing techniques such as rejection sampling for hard negative mining, safety-aware data concatenation for robustness, dynamic RL clipping schedules, and length-aware rewards balancing token efficiency with accuracy.

Empirical results indicate that GuardReasoner‑VL outperforms prior models by $19.27\%$ F1 margin, with the underlying reasoning process improving transparency and generalization against unseen safety threats. These advances are complementary to TGM vector transplant techniques and push the boundaries of scalable, explainable guardrail modeling for both LLMs and VLMs (Liu et al., 16 May 2025).

6. Security, Vulnerability, and Robustness

The security of TGM architectures is critically dependent on their resistance to adversarial bypasses. Research has shown that guard models—regardless of architecture, training regimen, or open/closed-source status—remain vulnerable to universal adversarial prefix attacks (PRP). The PRP methodology constructs a fixed adversarial prefix ( $\Delta_{f_G}$ ) that universally forces the guard to classify any harmful output as safe, and couples this with propagation prefixes ( $p_{\rightarrow \Delta_{f_G}}$ ) leveraging in-context learning to induce the base LLM to prepend the adversarial prefix.

Empirical case studies demonstrate attack success rates exceeding $90\%$ even where the attacker lacks direct access to the guard (transferability to closed-source guards such as GPT-3.5). This suggests that task-vector composition and prefix-based supervision, while highly effective for efficiency and extensibility, must be augmented with adversarial training, ensemble detection strategies, or non-LLM-based verification to close critical security gaps (Mangaokar et al., 24 Feb 2024).

7. Implications, Applications, and Future Research

Target Guard Models represent an efficient and flexible safety solution for LLM and VLM deployments, facilitating immediate integration across languages and architectures with minimal retraining. Data and compute requirements are reduced via parameter composition and streaming-aware training, promoting responsible and scalable AI moderation.

However, the persistence of universal adversarial vulnerabilities indicates an ongoing need for research into adversarial robustness, ensemble methods, and alternative moderation mechanisms beyond in-context learning and static prefix augmentation. Released datasets and model implementations (e.g., GuardReasoner‑VL train set and model code) provide community resources for benchmarking and refinement.

A plausible implication is that future TGMs will integrate explicit reasoning, adaptive moderation pipelines, and adversarial defense curricula, supporting transparent and resilient moderation frameworks in mission-critical applications.

Model	Safety Vector Approach	Streaming-Aware Training	Languages Supported
TGM (Editor’s term)	Guard Vector composition	Prefix SFT	EN, ZH, JA, KO
GuardReasoner‑VL	Explicit reasoning module	RL-driven, rejection	Multimodal (Text/Image)
Llama Guard, ShieldGemma	Baseline Guard Model	None or basic	Model-specific

TGMs, as demonstrated, enable rapid, robust, and extensible guardrail deployment while underscoring the need for continual improvements in adversarial defense and transparency mechanisms.