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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsNo published evidence we reviewed shows that video content faces fewer cyber threats from AI agents than text or other formats. The studies that exist document attacks on screenshot-reading web agents, on agents that combine images and text, and on video-based multimodal language models. Video is a distinct attack surface that needs its own analysis. It is not a safer format by default.
What “video content” can mean here
The phrase covers three different situations, and the answer changes with each one:
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- An agent that analyzes a video file. The agent receives frames, audio, subtitles or a transcript, and reasons about them. This is the case most of the attack research addresses.
- A video used as a source of instructions or evidence. A tutorial, a demo, or a clip that an agent is asked to trust and act on.
- A platform that hosts video. A site that stores and streams clips. The studies reviewed do not examine hosting risk. They say nothing about whether publishing video is inherently safer than publishing text.
Most of what follows concerns the first two cases, because that is where the published work sits.
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What the studies actually show
Four sources anchor the current evidence. They study different systems, use different attack methods, and report different kinds of numbers, so they cannot be ranked against each other or turned into a single risk score.
#1 Best Overall
| Source | Target studied | What was measured | Reported figure | What it does not show |
|---|---|---|---|---|
| WebInject, EMNLP 2025 (Association for Computational Linguistics, November 2025) | Multimodal web agents that act on webpage screenshots | Whether perturbed raw webpage pixels can make a rendered screenshot induce an attacker-specified action | Not stated as a single headline percentage in the source summary | Anything about video files; it concerns rendered webpages |
| CrossInject, arXiv preprint dated April 19, 2025 (not peer-reviewed at the time of the summary) | Multimodal agents that receive combined visual and textual input | Attack success when adversarial visual content is paired with textual instructions | At least 26.4% higher attack success rate than existing injection attacks, across the paper’s evaluated tasks | A real-world incident rate, or a measured video-versus-text difference |
| Study on transferability of adversarial attacks in video-based multimodal models, AAAI Proceedings, published March 14, 2026 (vol. 40, no. 7, pp. 5067–5075) | Video-based multimodal large language models on video question answering | Black-box attacks in zero-shot VideoQA tasks, including whether adversarial videos transfer to unseen models | 57.98% average attack success on MSVD-QA and 58.26% on MSRVTT-QA, as reported by the study authors | How often such attacks happen in deployed agents |
| Palo Alto Networks Unit 42, “AI Agents Are Here. So Are the Threats.” | Two functionally identical agent applications built on CrewAI and AutoGen | Vulnerabilities in agent design, tools, prompts and runtime environments | Not a percentage; qualitative findings | That every agent system has these weaknesses |
Screenshot-based prompt injection
WebInject, published in the Proceedings of EMNLP 2025 (pages 2010–2030), addresses agents that browse by looking at rendered pages rather than parsing code. The authors, Xilong Wang, John Bloch, Zedian Shao, Yuepeng Hu, Shuyan Zhou and Neil Zhenqiang Gong, describe the setup in their abstract:
“Multi-modal large language model (MLLM)-based web agents interact with webpage environments by generating actions based on screenshots of the webpages.”
The attack perturbs raw webpage pixels so that the rendered screenshot pushes the agent toward an action chosen by the attacker. The lesson is that what an agent sees can carry instructions a human reader would not notice. It is not evidence that every image or video input is vulnerable.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteCombining visual and text channels
CrossInject, a 2025 arXiv preprint by Le Wang and co-authors, pairs adversarial visual content with textual instructions so that the two channels reinforce each other. The authors report at least a 26.4% increase in attack success compared with existing injection attacks, across the tasks they evaluated. That is a comparison between attack methods in controlled experiments. It does not tell you how likely such an attack is against a system in operation, and because it is a preprint, its findings should be read as provisional until peer review is complete.
Rank #3
Adversarial attacks on video models
The AAAI study addresses the video-specific question directly. It reports that video-based multimodal large language models are vulnerable to adversarial examples in video-text tasks, and that attacks can transfer to models the attacker never trained on. The authors identify three limitations in existing attack methods: weak generalization of perturbations to video features, reliance on a sparse set of key frames, and incomplete integration of the visual and language modalities. They then introduce an image-to-video attack approach.
The 57.98% and 58.26% figures are black-box results on two video question-answering benchmarks, MSVD-QA and MSRVTT-QA, in a zero-shot setting. They show that the method works on those benchmarks. They do not measure exposure in a deployed product, and they should not be read as the chance that a given agent is compromised.
Design and permissions matter as much as format
Unit 42 tested two functionally identical agent applications, one built on CrewAI and one on AutoGen. It reports that most vulnerabilities it observed were not specific to either framework. They came from insecure design patterns, misconfiguration and unsafe tool integration. Its main findings were:
The Tool Desk
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- Prompt injection can leak data, misuse tools, or subvert the agent’s behavior.
- Vulnerable or misconfigured tools enlarge the attack surface.
- An unsecured code interpreter can allow arbitrary code execution or unauthorized access.
- Exposed credentials can enable impersonation or privilege escalation.
Unit 42 recommends layered safeguards across agents, tools, prompts and runtime environments, and states that no single mitigation is sufficient. These are that company’s findings and recommendations. They are not independent proof that every system shares these weaknesses.
Best Value
Why no format ranking is possible yet
A fair comparison of video against text would hold the agent model, tool permissions, deployment, and attacker capability constant while changing only the format. The useful outcomes would be attack success under the same task and threat model, and observed incidents over a stated period and region. None of the sources do this. Each one tests a different system with a different method, so the honest conclusion is that the evidence does not support ranking formats by overall risk.
How to assess an agent that processes video
If you are deciding whether to let an agent consume video, these questions matter more than the file type:
- How does the agent ingest the content? Check whether it extracts text, samples frames, runs OCR on on-screen text, or processes video directly with a multimodal model. Each route exposes different content to the model.
- What can the agent do with it? List every tool and action it can invoke, including code execution, file writes, email, purchases and API calls.
- Is the input trusted? Treat any video, frame, caption or transcript that an outside party can influence as attacker-controlled.
- Are permissions and credentials scoped? Confirm that the agent holds only the access each task needs and that secrets are not exposed to its prompts or tools.
- Are safeguards and monitoring in place? Look for logging of tool calls, restrictions on code execution, and a way to stop the agent when its actions diverge from the task.
These checks follow from the attack surfaces described in the studies above. They are a practical starting point, not a validated scoring method.
Reading the numbers responsibly
Every figure in this topic describes a controlled experiment: a particular model, a particular benchmark or task set, and a particular attack. None of them is an incident count or a probability for a real deployment, and none compares video with text under identical conditions. Quote them with their context, and be cautious about any headline that turns them into a claim about overall safety.
In short, video may be a different attack surface from text, with its own adversarial weaknesses, but the current evidence does not show it to be a smaller one.
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