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HFEPX · Eval paper review

Where Do Backdoors Live? A Component-Level Analysis of Backdoor Propagation in Speech Language Models

Alexandrine Fortier, Thomas Thebaud, Jesús Villalba, Najim Dehak +2 more

Published

Oct 1, 2025

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

15% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Apr 7, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

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Abstract

Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal. Despite their heterogeneous nature, SLMs are often studied end-to-end; how information flows through the pipeline remains obscure. We investigate this question through the lens of backdoor attacks. We first establish that backdoors can propagate through the SLM, leaving all tasks highly vulnerable. From this, we design a component analysis to reveal the role each component takes in backdoor learning. We find that backdoor persistence or erasure is highly dependent on the targeted component. Beyond propagation, we examine how backdoors are encoded in shared multitask embeddings, showing that poisoned samples are not directly separable from benign ones, challenging a common separability assumption used in filtering defenses. Our findings emphasize the need to treat multimodal pipelines as intricate systems with unique vulnerabilities, not solely extensions of unimodal ones.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal.
  • Despite their heterogeneous nature, SLMs are often studied end-to-end; how information flows through the pipeline remains obscure.
  • We investigate this question through the lens of backdoor attacks.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal.
  • Despite their heterogeneous nature, SLMs are often studied end-to-end; how information flows through the pipeline remains obscure.
  • We investigate this question through the lens of backdoor attacks.

Why it matters for eval

  • Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.