Steganography, a data hiding technique has trended into hiding the malware within digital media, giving rise to stegomalware. Specifically, digital images pose a potential threat due to their massive use, innocuous appearance, and ability to hide data without noticeably degrading the quality. Detecting malicious image steganography is a challenge for intrusion detection systems or static detection that lack analyzing the pixels of an image. This paper presents a tool in python to detect malware in widely used JPEG image format. The existing methods have mostly focused on finding steganography artifacts or used feature-based analysis that lacks revealing the hidden malign data. Unlike existing ones, the proposed tool alongside classification locates malicious content in JPEG images with revealing the found malign data along with its location as the output. This functionality to the best of our knowledge has not been found in the available literature. The tool has analyzed three types of JPEG images: malicious, benign, and stego images. Though malicious images are also stego ones, the paper refers to those hiding non-malicious data as the stego images. This is to evaluate the effectiveness of our tool in classifying the images with malicious and non-malicious data hidden. As a result, the tool has attained a low False Negative Rate (FNR) of 0.08 and False Positive Rate (FPR) of 0.001 with a better detection rate relative to state-of-the-art techniques. Indeed, it has predicted all stego images as non-malicious. Also, the paper has assessed the detection of Windows applications containing stegomalware.