Zoho Campaigns A.I CyberSecurity Scoring
12/03/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Zoho Campaigns in 2026.
No incidents recorded for Zoho Campaigns in 2026.
No incidents recorded for Zoho Campaigns in 2026.
RRD provides a complete portfolio of marketing, packaging, print and business services to the world’s most respected brands, including 91% of the Fortune 100. Our proprietary technology, advanced data analytics and established expertise fuel organizational decision-making, from strategy through execution. With operations in 30 countries, global organizations and regulated industries trust RRD to reduce complexity and drive audience connections across the entire customer journey.
Latest updates, reports, and threat intel affecting the global network.
The North Korean threat actor known as ScarCruft has been attributed to a fresh set of tools, including a backdoor that uses Zoho WorkDrive...
North Korea-linked threat group APT37 has launched a sophisticated new campaign using a fresh set of custom malware tools specifically...
Active exploitation of a remote code execution (RCE) vulnerability in SolarWinds Web Help Desk (WHD) is accelerating, with attackers rapidly...
DanaBot resurfaces, resumes Windows infections after six-month shutdown, Mass phishing campaign targets hotel bookings with 4300 fake sites,...
Odisha is launching a month-long cyber security awareness campaign to combat a 1917% surge in cyber crimes and a 1622% rise in financial...
Strong customer communications and effective prospecting campaigns are integral to the success of any business, and email is widely seen as...
Modified AllaKore RAT and Ghost Crypt crypter target Mexican entities and global victims for financial fraud.
A targeted attack against a U.S.-based certified public accounting firm was discovered in May 2025 by cybersecurity experts.
The US Federal Bureau of Investigation (FBI) has issued a fresh alert warning law firms and cybersecurity professionals about ongoing cyber threat activity...
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm/entrypoints/anthropic/api_router.py (the POST /v1/messages and POST /v1/messages/count_tokens handlers), the Server-Sent Events streaming converter in vllm/entrypoints/anthropic/serving.py, and the realtime speech-to-text WebSocket in vllm/entrypoints/speech_to_text/realtime/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to ~14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1.
vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.
curl -i -X GET 'https://api.rankiteo.com/underwriter-getcompany-history?
linkedin_id=axa' -H 'apikey: YOUR_API_KEY_HERE'
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