Is there a difference between editing HTTP messages manually or with burp for example? (WebGoat HTTP intercept exercise “problem”)

I am diving now into WebGoat, there’s this little exercise in the “general” tab calle d “http proxies” which asks you to use zap/burp to intercept and modify a request, this is what is being us asked.

enter image description here

I understood what is being us asked to do, but I don’t understand why if I change it manually it doesn’t work, whereas if I use the burp button “change request method” does, as it’s the same text at the end, am I missing something?

This is the original request

And here after I modify it with the button

The only difference is that I write that GET string manually and then add the ?changeMe=Requests+are+tampered+easily I don’t understand why it won’t work and it’s driving me nuts.

Oh and another thing, if I enter the x-request-intercepted:true below Cookie sometimes wont work, is it being considered body or what? (there isn’t a break line)

Why is JavaScript executed manually from the browser console not allowed to access everything?

Why is JavaScripts executed manually from the browser console not allowed to access “everything”? Especially the “visited” status (see this question) of links? What kind of security threat would that pose?

Usually, users have full access to their environment, sometimes with a little bump in the form of entering the root password or similar. Why is this an exception?

(I am not saying that scripts downloaded from a web page should have this access. I understand why that is a threat to the user’s privacy etc.)

Manually converting TensorFlow models to Mathematica

Is

Mathematica (v12, my uninformed attempt at manual conversion)

h = StringSplit@Import["https://raw.githubusercontent.com/IBM/tensorflow-hangul-recognition/master/labels/2350-common-hangul.txt"]; n = NetChain[{(*2*)    ConvolutionLayer[032, 5], Ramp, PoolingLayer[2, 2],    ConvolutionLayer[064, 5], Ramp, PoolingLayer[2, 2],    ConvolutionLayer[128, 3], Ramp, PoolingLayer[2, 2],    FlattenLayer[],    LinearLayer[1024],    Ramp,    DropoutLayer[],    LinearLayer[h // Length],    SoftmaxLayer[]    },   "Input"  -> NetEncoder[{"Image", {64, 64}, ColorSpace -> "Grayscale"}],   "Output" -> NetDecoder[{"Class", h}]   ] 

NetChain[ <> ]

a good conversion of

Tensorflow (v1?, source network, excerpt from complete github file)

    # First convolutional layer. 32 feature maps.     W_conv1 = weight_variable([5, 5, 1, 32])     b_conv1 = bias_variable([32])     x_conv1 = tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1],                            padding='SAME')     h_conv1 = tf.nn.relu(x_conv1 + b_conv1)       # Max-pooling.     h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1],                              strides=[1, 2, 2, 1], padding='SAME')       # Second convolutional layer. 64 feature maps.     W_conv2 = weight_variable([5, 5, 32, 64])     b_conv2 = bias_variable([64])     x_conv2 = tf.nn.conv2d(h_pool1, W_conv2, strides=[1, 1, 1, 1],                            padding='SAME')     h_conv2 = tf.nn.relu(x_conv2 + b_conv2)       h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1],                              strides=[1, 2, 2, 1], padding='SAME')       # Third convolutional layer. 128 feature maps.     W_conv3 = weight_variable([3, 3, 64, 128])     b_conv3 = bias_variable([128])     x_conv3 = tf.nn.conv2d(h_pool2, W_conv3, strides=[1, 1, 1, 1],                            padding='SAME')     h_conv3 = tf.nn.relu(x_conv3 + b_conv3)       h_pool3 = tf.nn.max_pool(h_conv3, ksize=[1, 2, 2, 1],                              strides=[1, 2, 2, 1], padding='SAME')       # Fully connected layer. Here we choose to have 1024 neurons in this layer.     h_pool_flat = tf.reshape(h_pool3, [-1, 8*8*128])     W_fc1 = weight_variable([8*8*128, 1024])     b_fc1 = bias_variable([1024])     h_fc1 = tf.nn.relu(tf.matmul(h_pool_flat, W_fc1) + b_fc1)       # Dropout layer. This helps fight overfitting.     keep_prob = tf.placeholder(tf.float32, name=keep_prob_node_name)     h_fc1_drop = tf.nn.dropout(h_fc1, rate=1-keep_prob)       # Classification layer.     W_fc2 = weight_variable([1024, num_classes])     b_fc2 = bias_variable([num_classes])     y = tf.matmul(h_fc1_drop, W_fc2) + b_fc2       # This isn't used for training, but for when using the saved model.     tf.nn.softmax(y, name=output_node_name) 
  1. How can the Mathematica model improved to match the Tensorflow version exactly?
  2. Is there a resource anywhere to learn the correspondences between the two?
  3. Specifically, I am not sure about
    1. padding='SAME' – how to stay true to this in Mathematica?
    2. tf.nn.relu(x_conv1 + b_conv1) == Ramp?
    3. tf.matmul == LinearLayer?
    4. FlattenLayer[]
    5. DropoutLayer[.5] (tensor flow switches between 0.5 and 1.0, see complete file linked above)

I feel like I am making critical mistakes somewhere. The resulting network is too sensitive.

Does a kineticist’s telekinetic blast literally means manually throwing a physical object?

Is a kineticist’s telekinetic blast literally means manually throwing a physical object?

Telekinetic Blast

Element(s) aether; Type simple blast (Sp); Level —; Burn 0 Blast Type: physical; Damage bludgeoning, piercing, or slashing

You throw a nearby unattended object at a single foe as a ranged attack. The object must weigh no more than 5 pounds per kineticist level you possess. If the attack hits, the target and the thrown object each take the blast’s damage. Since the object is enfolded in strands of aether, even if you use this power on a magic weapon or other unusual object, the attack doesn’t use any of the magic weapon’s bonuses or effects; it simply deals your blast damage. Alternatively, you can loosen the strands of aether in order to deal damage to both the object and the target as though you had thrown the object yourself (instead of dealing your normal blast damage).

You substitute your Constitution modifier for your Strength modifier if throwing the object would have added your Strength modifier on the damage roll, and you don’t take the –4 penalty on the attack roll for throwing an object that wasn’t designed to be thrown. In this case, the object’s special effects apply (including effects from its materials), and if the object is a weapon, you must be proficient with it and able to wield it with one hand; otherwise, the item deals damage as a one-handed improvised weapon for a creature of your size.

I always though it functions much like the Telekinesis spell using the Violent Thrust option.

Violent Thrust: Alternatively, the spell energy can be spent in a single round. You can hurl one object or creature per caster level (maximum 15) that are within range and all within 10 feet of each other toward any target within 10 feet per level of all the objects. You can hurl up to a total weight of 25 pounds per caster level (maximum 375 pounds at 15th level).

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