Pith. sign in

REVIEW 2 cited by

UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.13141 v1 pith:WTS25IJJ submitted 2025-02-18 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords attackspromptadversarialbackdoorinjectionllmsmodelsuniguardian
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prompts or models to generate harmful outputs. In this paper, departing from traditional deep learning attack paradigms, we explore their intrinsic relationship and collectively term them Prompt Trigger Attacks (PTA). This raises a key question: Can we determine if a prompt is benign or poisoned? To address this, we propose UniGuardian, the first unified defense mechanism designed to detect prompt injection, backdoor attacks, and adversarial attacks in LLMs. Additionally, we introduce a single-forward strategy to optimize the detection pipeline, enabling simultaneous attack detection and text generation within a single forward pass. Our experiments confirm that UniGuardian accurately and efficiently identifies malicious prompts in LLMs.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff

    cs.IT 2026-04 unverdicted novelty 6.0 of 10

    Synset-based reconstruction and synonymous variational inference are claimed to derive the distributional divergence in RDP and unify it with classical rate-distortion theory.

  2. Defending Against Prompt Injection With a Few DefensiveTokens

    cs.CR 2025-07 conditional novelty 6.0 of 10

    Five optimized token embeddings prepended to the prompt reduce prompt-injection attack success to near zero on standard benchmarks while preserving most model utility.

Pith tools