Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 17
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey | Route /signal-canvas/adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey",
    "query_text": "Summarize Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey",
  "normalized_query": "2603.27918",
  "route": "/signal-canvas/adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey",
  "paper_ref": "adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 7

References: 17

Proof: Verification pending

Freshness state: computing

Source paper: Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

PDF: https://arxiv.org/pdf/2603.27918v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-31T20:53:21.783Z

Signal Canvas receipt window

Not build-ready: Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

/buildability/adversarial-attacks-on-multimodal-large-language-models-a-comprehensive-survey

Ignoreblocked

Subject: Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
LLMs present an expanded attack surface where threats can arise not only from weaknesses within individual modality processing but, crucially, from the complex interplay and fusion mechanisms between the combined modalities
Implicationpartial
Directly stated in the paper with a specific reference to the source of the vulnerability.
Verificationpartial
partial
Evidencepartial
Cross-modal prompt injection exploits LLMs’ instruction-following nature by embedding malicious commands
Implicationpartial
Directly listed as a specific attack vector in the paper's analysis of threats.
Verificationpartial
partial
Evidencepartial
A critical vulnerability arises from the shared high-dimensional embedding space... Carefully crafted inputs that appear benign at the perceptual level can induce latent representations that are semantically misleading after fusion
Implicationpartial
Explicitly described as a 'critical vulnerability' with a clear mechanism explained.
Verificationpartial
partial
Evidencepartial
jailbreak attacks explicitly target harmlessness and policy compliance... safety alignment is commonly strongest for text, while non-text modalities (images, audio, video) provide alternate channels through which
Implicationpartial
Directly stated as the objective of jailbreak attacks and the reason for their effectiveness in MLLMs.
Verificationpartial
partial
Evidencepartial
Discrete trigger attacks rely on structured artifacts... that function as reusable control signals... once constructed, the same artifact can be deployed across diverse inputs, prompts, or tasks to induce consistent integrity failures
Implicationpartial
Clearly defined and contrasted with other attack types, with specific characteristics listed.
Verificationpartial
partial
Evidencepartial
end-to-end certification of multimodal fusion, autoregressive decoding, and instruction-following behavior remains challenging, leaving representation-level exploits and higher-level safety or control attacks largely outside the scope of current guarantees
Implicationpartial
Directly stated as a current limitation of defense techniques.
Verificationpartial
partial
Evidencepartial
Organizing attacks by primary adversarial objective instead captures the type of system-level failure induced—such as loss of correctness, safety, control, or training reliability, independent of how the attack is instantiated
Implicationpartial
Directly argued as a superior organizational principle for understanding attacks, though presented as the paper's analytical framework rather than a proven result.
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey

Use Signal Canvas as the narrative proof surface

Use this Signal Canvas via API or MCP

Signal Canvas proof surface