Prompt Injection Detection Framework

Updated 22 November 2025

Prompt Injection Detection Framework is a system that integrates advanced algorithms, federated learning, and privacy protocols to identify and mitigate adversarial prompt injections in LLM environments.
It employs secure aggregation, differential privacy, and robust aggregation techniques to safeguard sensitive data and maintain high detection accuracy.
The framework addresses threats from external attackers and malicious federated learning clients, ensuring system resilience and data integrity.

A prompt injection detection framework is a collection of algorithms, threat models, cryptographic mechanisms, and system architectures designed to identify, mitigate, and resist prompt injection attacks in distributed LLM environments—most notably in privacy-sensitive federated or cross-silo settings. The framework must balance attack detection efficacy against potential privacy risks and communication/computation constraints. Recent advances focus on privacy-preserving, distributed approaches using federated learning (FL), secure aggregation, differential privacy (DP), and robust aggregation against adversarial and Byzantine clients.

1. Threat Model and Security Problem Formulation

Prompt injection attacks are adversarial inputs crafted to subvert LLM behavior by embedding unauthorized commands or content in user prompts, often to bypass content filters, extract confidential information, or inject toxic instructions (Jayathilaka, 15 Nov 2025). In federated settings, prompt injection arises both as a localized threat (targeting per-client models) and as a global risk through model poisoning, where malicious clients inject adversarial examples during FL training (Lee et al., 30 Jan 2025).

The principal adversaries in this context are:

External attackers crafting prompts to distributed LLMs to elicit misbehavior.
Malicious FL clients injecting poisoned gradients or updates to either degrade detection or insert backdoors (Han et al., 2023 Jiang et al., 13 May 2025).

A federated prompt injection detection system aims to:

Identify and flag adversarial prompts with high precision and recall.
Prevent model-level transfer of injection vulnerabilities across participating clients.
Maintain privacy of client prompt data, avoiding centralized exposure of raw logs or embeddings.

2. System Architecture and Federated Workflow

A typical prompt injection detection framework in federated environments is structured in a star topology consisting of $K$ client nodes and a central server (aggregator) (Wang et al., 25 Feb 2025 Jayathilaka, 15 Nov 2025). Each client maintains local prompt logs and performs local model training or inference. The core architectural features are:

Data Locality: Clients retain all raw prompts and sensitive features locally; only model parameters or aggregate statistics are transmitted.
Federated Learning Loop: The system adopts synchronous or asynchronous FL protocols (e.g., FedAvg), where each client trains a local detector (e.g., embedding-based classifier) on its private prompt dataset and sends an update to the server for aggregation (Jayathilaka, 15 Nov 2025).
Secure Communication: All transmissions are conducted over authenticated, encrypted channels (e.g., HTTP/2 + TLS) (Wang et al., 25 Feb 2025).

Example: Embedding-Based FL Detection Pipeline

Client-side: Encode each prompt $x$ as embedding $e = f(x)$ (e.g., using SentenceTransformer models (Jayathilaka, 15 Nov 2025)).
Train a local classifier (e.g., logistic regression) to separate benign from injection prompts on the local dataset.
Transmit updated model weights to the central server.
Server: Aggregate client updates (often via weighted averaging), possibly applying secure aggregation, and broadcast the updated model.
Each client uses the aggregated detector locally to flag suspicious prompts, triggering audit or further response.

This pipeline ensures that detection can be performed without ever exposing raw prompts, mitigating the principal privacy threat of prompt centralization (Jayathilaka, 15 Nov 2025).

3. Privacy and Robustness Mechanisms

3.1 Secure Aggregation and Differential Privacy

Secure Aggregation: Clients mask their model updates using random noise or cryptographic protocols so that only the aggregate, not individual updates, is visible to the server (Wang et al., 25 Feb 2025 Jayathilaka, 15 Nov 2025). A protocol may use pairwise masks, as in Bonawitz et al., to cancel noise in aggregation.
Differential Privacy (DP): Each client can add calibrated, task-specific Gaussian noise to the shared update:

$\hat{w}_i = w_i + \mathcal{N}(0, \sigma^2 I)$

with $\sigma$ tuned for a target $(\epsilon, \delta)$ -DP guarantee (Wang et al., 25 Feb 2025). This approach bounds the information that can be inferred about any individual prompt, even from model gradients.

3.2 Byzantine and Adversarial Robustness

Robust Aggregation: To resist malicious clients, robust aggregation methods such as trimmed mean, Krum, and geometric median can be employed (Han et al., 2023 Pang et al., 17 Feb 2025). These dampen the effect of poisoned updates by removing or downweighting outliers in the update distribution.
Embedding-Space Adversarial Training: Frameworks like FedEAT introduce min–max adversarial training in embedding space, making detectors robust to adversarially perturbed prompts. This is performed as a projected gradient ascent/descent routine within each local training step, targeting the worst-case local loss due to embedding-space perturbations (Pang et al., 17 Feb 2025).

4. Detection Methodologies and Fingerprinting

Two principal approaches for detection and cross-system intelligence sharing have emerged:

4.1 Embedding-Based Classification

Clients use powerful embedding models to project each prompt into a high-dimensional vector space (e.g., $\mathbb{R}^d$ ). Detection is performed by an embedding-based classifier (e.g., logistic regression), where the learned weight vector $w$ and bias $b$ are iteratively updated and aggregated via FedAvg (Jayathilaka, 15 Nov 2025).

Performance: In controlled studies, a federated model trained on prompt injection data achieved perfect detection accuracy (100.0%) and AUC on a held-out test set, matching centralized training results (Jayathilaka, 15 Nov 2025).

4.2 Privacy-Preserving Attack Fingerprinting

To facilitate secure cross-service correlation of injection patterns, frameworks like BinaryShield convert suspicious prompts into non-invertible, privacy-preserving binary fingerprints through a pipeline of:

PII redaction,
Semantic embedding,
Sign-based binary quantization,
Randomized response for local DP (Gill et al., 6 Sep 2025).

These fingerprints can be exchanged and queried via Hamming-similarity search, enabling rapid detection of known or paraphrased injections without exposing sensitive content. Empirical results demonstrate an F1-score of 0.94, 64x storage reduction, and 38x faster search compared to prior baselines (Gill et al., 6 Sep 2025).

5. Evaluation Metrics and Empirical Results

Frameworks are evaluated on detection accuracy, privacy guarantees, false positive/negative rates, and computational efficiency. Representative metrics include:

Accuracy: Proportion of correctly classified prompts over the test set (Jayathilaka, 15 Nov 2025).
Precision, Recall, F1-score: Especially for imbalanced benign/adversarial splits or in cross-system fingerprinting (Gill et al., 6 Sep 2025).
AUC: Area under the ROC curve (Jayathilaka, 15 Nov 2025).
Empirical Privacy/Utility Trade-off: Impact of DP noise or fingerprint distortion parameter ( $\alpha$ for randomized response) on detection F1 and storage/search efficiency (Gill et al., 6 Sep 2025).

Reported results:

Perfect accuracy (100%) in both centralized and federated embedding-based detection with moderate-scale datasets (Jayathilaka, 15 Nov 2025).
F1=0.94 for BinaryShield cross-service detection, compared to F1=0.77 for SimHash (Gill et al., 6 Sep 2025).
Federated approaches incur negligible accuracy or convergence penalty when properly configured (Jayathilaka, 15 Nov 2025).

6. Security Analysis, Limitations, and Future Directions

Security Analysis

The frameworks guarantee that no raw prompt data or embeddings are exposed outside the client device.
Secure aggregation and DP limit inference attacks from both honest-but-curious servers and external interceptors (Wang et al., 25 Feb 2025).
Robust aggregation and adversarial training techniques mitigate the risk of model poisoning by Byzantine clients (Han et al., 2023 Pang et al., 17 Feb 2025).

Limitations

Dataset Scale: Many published results are limited to small, curated datasets; scalability and generalization to open-domain prompt distributions are not fully demonstrated (Jayathilaka, 15 Nov 2025).
Model Simplicity: Most prototype frameworks use logistic regression or shallow classifiers; advanced attacks may defeat simple detectors (Jayathilaka, 15 Nov 2025).
Robustness to Advanced Attacks: Adaptive and subtle prompt injections, collaborative poisoning, or adaptive inference attacks remain possible vectors (Lee et al., 30 Jan 2025 Han et al., 2023).
Privacy Protocol Coverage: Current implementations may lack integration of state-of-the-art cryptographic safeguards (e.g., full homomorphic encryption); DP budget tuning and end-to-end security are open challenges.

Future Directions

Adoption of transformer-based prompt injection detectors trained in federated or cross-silo regimes.
Systematic integration of secure aggregation, homomorphic encryption, and strong DP in production deployments.
Expansion to real-world, dynamic LLM services and IoT/edge-distributed settings (Gill et al., 6 Sep 2025 Otoum et al., 22 Apr 2025).
Attack benchmarking on evolving prompt injection strategies and large-scale datasets, with formal privacy and utility accounting.
Hardening detection systems against adaptive or poisoning-capable adversaries, along with usability-focused scalability studies.

7. Connections to Broader Federated LLM Security

Prompt injection detection frameworks form a critical subset of the security apparatus for federated LLM systems, overlapping with defenses against gradient inversion, membership inference, and model/backdoor poisoning (Jiang et al., 13 May 2025). Techniques developed in this context—including robust aggregation, DP, and embedding-based anomaly detection—are applicable across the broader landscape of federated security for LLMs, reinforcing privacy and trust in distributed AI deployments.