Cross-Modal Conflict Injection (CMCI)

• Cross-Modal Conflict Injection is a set of adversarial methods that inject conflicting semantic signals across multiple modalities to exploit vulnerabilities in fusion systems.
• CMCI frameworks like IJA and CrossFire leverage techniques such as LSB steganography and PGD optimization to manipulate multimodal embedding alignments.
• Empirical studies show high attack success rates in domains such as vision-language, medical AI, and autonomous agents, highlighting critical safety concerns.

Cross-Modal Conflict Injection (CMCI) denotes a collection of adversarial methodologies targeting multimodal AI architectures, wherein contradictory or misaligned semantic content is injected across separate modalities—such as image, text, audio, or video—to induce model outputs that violate intended constraints, mislead downstream reasoning, or bypass safety filters. CMCI exploits the fusion and alignment mechanisms at the heart of modern multimodal models, coercing agents, retrieval systems, or embedding frameworks to reconcile semantic conflicts in favor of attacker-chosen payloads. Recent empirical and formal results indicate that CMCI bypasses both superficial content filters and robust alignment protocols across domains including vision-language modeling, medical retrieval-augmented generation (RAG), autonomous agent control, multimodal embedding, and contradiction detection.

1. Theoretical Foundations and Formal Definition

CMCI is defined by the intentional, adversarial creation of conflicting semantic directives across modalities. In vision-LLMs (VLMs), for example, CMCI structures a benign textual prompt $Q'$ and an adversarially embedded visual input $I'$, such that the fusion process $F_{\theta}(I', Q') \rightarrow y$ resolves in favor of the malicious visual instruction $Q_{\text{mal}}$—even when $Q'$ is safe under text-only filters (Wang et al., 22 May 2025). In medical AI, CMCI pairs an image $I^*$ with an adversarial report $T^*$ which remain close in semantic distance $D_{\text{sem}}((I,T),(I^*,T^*)) \leq \epsilon$, yet force clinical generators $\mathcal{G}$ to produce misleading answers due to preserved retrieval alignment and deliberate cross-modal disagreement (Zuo et al., 24 Aug 2025).

Similarly, in multimodal embedding attacks, a perturbation $\delta$ is added to input $x^{(m)}$ so that its embedding matches that of a target $y_t^{(m')}$ from another modality, creating a cross-modal illusion: the observable sample remains unchanged for humans, but the embedding causes downstream systems to treat it as semantically linked to the adversary’s chosen target (Zhang et al., 2023, Dou et al., 10 Sep 2024).

In agentic models interacting with dynamic environments, CMCI is operationalized by selectively corrupting specific observations (e.g., audio vs. vision vs. text) so that cues for optimal action contradict each other, sharply degrading agent performance and revealing brittle fusion (Bie et al., 6 Aug 2025).

2. Algorithms and Frameworks for CMCI Realization

A range of frameworks instantiate CMCI through both black-box and white-box optimization:

• IJA (Implicit Jailbreak Attack): Performs adversarial suffix generation over prompts, benign prompt rewriting, LSB steganographic image embeddings, and iterative template optimization. The adversarial payload ($Q_{\text{mal}} \oplus s$) is concealed via bitwise operations on image LSBs and guided by model feedback (Wang et al., 22 May 2025).
• CrossFire: Applies cross-modal transformation $T$ to map target inputs to the modality of the victim sample, then minimizes angular distance between normalized embeddings via PGD under an $\ell_\infty$ perturbation constraint (Dou et al., 10 Sep 2024).
• CrossInject: Employs Visual Latent Alignment (aligns visual features to adversarial text/image via surrogate encoders and SSA-CWA), Textual Guidance Enhancement (meta-prompting to infer defensive system prompt, Greedy Coordinate Gradient adversarial suffix optimization), and combined perturbation strategies (Wang et al., 19 Apr 2025).
• Medical CMCI: Alternates gradient-based perturbations on images and reports, under a stealthiness constraint in CLIP space and alignment objectives to maximize misleading answer likelihood, followed by plausibility and reranking checks (Zuo et al., 24 Aug 2025).
• Adversarial Illusions: Generalizes CMCI for any multi-modal embedding, optimizing $$\delta^{*} = \arg\min_{\|\delta\|_\infty \leq \epsilon}\{1 - \cos(\theta^{(m)}(x+\delta), \theta^{(m')}(y_t))\}$$ via PGD or Square-Attack (query-based) methods. Ensemble, transfer, and certifiable robustness constraints are discussed in detail (Zhang et al., 2023).

The table below summarizes notable frameworks and characteristic optimization strategies:

Framework	Modalities	Optimization Objective
IJA	Vision, Text	LSB embed, adversarial suffix
CrossFire	Vision, Audio, Text	PGD: angular embedding loss
CrossInject	Vision, Text, Ext.	surrogate fusion alignment, GCG
MedThreatRAG	Vision, Text	altern. gradients, CLIP align
Adversarial Illusions	Any	cosine distance, PGD/ensemble

3. Experimental Results and Empirical Manifestations

CMCI’s effectiveness has been validated across model families and application domains:

• Jailbreak Attacks: The IJA framework achieves >90% Attack Success Rate (ASR) on GPT-4o and Gemini-1.5 Pro, requiring only ~3 queries per attack. Bypass rate of safety filters approaches 97.7% (Wang et al., 22 May 2025).
• Embeddings: CrossFire increases ASR by 20–40 points vs. baselines on ImageNet, AudioSet, MS-COCO, STL-10, and WikiArt datasets, aligning perturbed data with adversarial targets. Standard defenses (JPEG, SDE denoising, up/downsampling) fail to drop ASR below ~0.75 (Dou et al., 10 Sep 2024). Adversarial illusions transfer robustly across distinct encoders and survive feature-distillation-based defenses when optimized for post-processing (Zhang et al., 2023).
• Medical RAG: MedThreatRAG reduces answer F1 scores by up to 27.66 pp (IU-Xray) and 16.52 pp (MIMIC-CXR) under cross-modal poisoning, far exceeding the impact of uni-modal or baseline attacks. Generator-stage interventions deliver the largest degradation (Zuo et al., 24 Aug 2025).
• Autonomous Agents and Games: The OmniPlay benchmark reveals catastrophic drops in task performance under CMCI. For pathfinding tasks, mean step count triples with audio or text conflict injection (e.g., Gemini 2.5 Pro: 36.2 [clean] to 133.7 [audio conflict]); win rates in competitive environments collapse by 50–80% under auditory CMCI (Bie et al., 6 Aug 2025).
• Contradiction Detection: CLASH benchmark exposes models’ inability to reliably detect cross-modal conflicts, with open-source models falling below 20% accuracy and closed-source models >85%. Systematic bias toward particular modalities hinders robust arbitration in ambiguous scenarios (Popordanoska et al., 24 Nov 2025).

4. Mechanistic Insights: Fusion, Decision Bias, and Model Weakness

CMCI exploits fundamental weaknesses in multimodal fusion. Agents or models must arbitrate when presented with contradictory semantic evidence:

• Fusion modules frequently privilege one modality (often the less filtered or more privileged channel), allowing hidden adversarial directives to dominate (Wang et al., 22 May 2025, Wang et al., 19 Apr 2025).
• Retrievers using joint embedding spaces cannot distinguish genuine semantic similarity from adversarial alignment since high inner-product or cosine similarity is necessary for normal retrieval (Zuo et al., 24 Aug 2025).
• Models demonstrate modality bias and fail to learn content-level contradiction detection without targeted fine-tuning. For example, Qwen models (3-VL) favor image cues in case of ambiguity, while InternVL and LLaVA variants bias toward text (Popordanoska et al., 24 Nov 2025).
• CMCI may induce the “less is more” paradox: disabling conflicted sensory channels leads to improved performance due to avoidance of catastrophic fusion errors (Bie et al., 6 Aug 2025).

5. Defenses: Detection, Auditing, and Alignment Mechanisms

Empirical results consistently show that naive pre-processor defenses (JPEG compression, denoising, transformations) are insufficient against CMCI. The following advanced strategies have been proposed:

• Steganalysis: Detect unnatural LSB distributions or entropy patterns in visual input, targeting image-based attacks (Wang et al., 22 May 2025).
• Consistency Checks: Enforce logical entailment or contradiction scoring between paired modalities (image-text) to block outputs when inconsistency is high (Zuo et al., 24 Aug 2025).
• Anomaly Detection via Augmentation Consistency: Flag inputs with embedding sensitivity to random augmentations, though optimized attacks evade via differentiable preprocessing (Zhang et al., 2023).
• Cross-Modal Adversarial Training: Jointly robustify multiple encoders against alignment-attacks but trade off utility and coverage (Wang et al., 19 Apr 2025).
• Prompt-Consistency Auditing/DCI: Explicitly compare extracted instructions from all modalities for mutual exclusivity prior to completion or action (Wang et al., 22 May 2025).
• Operational Safeguards: Maintain provenance of KB updates, enable rollback and real-time alerts for high-retrieval/generation inconsistency (Zuo et al., 24 Aug 2025).
• Benchmark-driven Fine-Tuning: CLASH demonstrates that conflict-detection accuracy for open-source models can be dramatically improved via quality-controlled, contradiction-rich data (Popordanoska et al., 24 Nov 2025).

6. CMCI Across Domains: Applications and Impact

CMCI’s techniques and vulnerabilities have cross-domain relevance:

• Safety-Critical AI: Medical RAG platforms, autonomous vehicle assistants, and agentic planners are shown to be vulnerable even under practical deployment constraints (black-box, API access) (Zuo et al., 24 Aug 2025, Wang et al., 19 Apr 2025).
• General Embedding Models: Foundational multi-modal encoders (ImageBind, AudioCLIP, proprietary Titan) are compromised for zero-shot classification, search, and generation tasks (Zhang et al., 2023, Dou et al., 10 Sep 2024).
• Contradiction Detection Benchmarks: CLASH and OmniPlay provide systematic stress-testing tools for robustness research in contradiction arbitrage and cross-modal reasoning (Popordanoska et al., 24 Nov 2025, Bie et al., 6 Aug 2025).

7. Future Directions and Open Challenges

Persistent open problems include design of certifiable robust embedding schemes accommodating necessary invariance, adversarial training integrating cross-modal conflict exemplars, and scalable anomaly detection. Given CMCI’s resilience against legacy defenses and its ability to syphon control in agentic and retrieval settings, future systems require new architectures for modality arbitration, semantic consistency scoring, and provenance-based safekeeping. Benchmark-driven dataset curation and contrastive contradiction learning represent promising directions; however, core vulnerabilities remain in the organic fusion logic underpinning multimodal “foundation” models.

---

CMCI represents a pivotal adversarial paradigm in multimodal AI security, with demonstrated success against current and next-generation models. Its implications span model safety, reliability, and trust across a spectrum of real-world applications, necessitating robust, holistic defense protocols and ongoing critical analysis as highlighted by the referenced literature (Wang et al., 22 May 2025, Zuo et al., 24 Aug 2025, Dou et al., 10 Sep 2024, Wang et al., 19 Apr 2025, Popordanoska et al., 24 Nov 2025, Bie et al., 6 Aug 2025, Zhang et al., 2023).