Techniques for synthesizing security exploits via self-amplifying deep learning are provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a probabilistic model based on an evaluation of one or more first payloads included in a first group of payloads. The computer implemented method can also comprise determining, by the system, based on the probabilistic model, that at least one first payload from the first group of payloads is invalid. Additionally, the computer implemented method can comprise, generating, by the system, a second group of payloads based on removing the at least one invalid first payload from the first group of payloads.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: generating, by a system operatively coupled to a processor, a probabilistic model based on an evaluation of one or more first payloads included in a first group of payloads; determining, by the system, based on the probabilistic model, that at least one first payload from the first group of payloads is invalid; and generating, by the system, a second group of payloads based on removing at least one invalid first payload from the first group of payloads. 2. The computer-implemented method of claim 1, further comprising: outputting, by the system, the second group of payloads. 3. The computer-implemented method of claim 1, further comprising: generating, by the system, an input sequence representing one or more vectors based on encoding a payload of the second group of payloads. 4. The computer-implemented method of claim 3, further comprising: applying, by the system, the input sequence to a deep learning engine of the system. 5. The computer-implemented method of claim 4, further comprising: generating, by the system, a sequence of probability vectors in response to applying the input sequence to a multilayer recurrent network, wherein the multilayer recurrent network comprises the probabilistic model. 6. The computer-implemented method of claim 5, further comprising: generating a third group of payloads based on the sequence of probability vectors. 7. The computer-implemented method of claim 6, further comprising merging the third group of payloads with the second group of payloads. 8. The computer-implemented method of claim 1, further comprising: determining, by the system, that at least one of the one or more first payloads is valid based on executing the at least one of the one or more first payloads on a software application that retrieves and presents information from a network of resources based on a uniform resource identifier. 9. A computer program product for synthesizing security exploits via self-amplifying deep learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing component to cause the processing component to: determine, by the processing component, that a payload generates a valid result; atomize, by the processing component, the payload into one or more atomic elements; map, by the processing component, the one or more atomic elements to one or more identifiers; and supply, by the processing component, the one or more identifiers to a deep learning engine. 10. The computer program product of claim 9, wherein the one or more identifiers are first identifiers, and wherein the program instructions are further executable by the processing component to cause the processing component to generate a synthesized payload based on the first identifiers supplied to the deep learning engine. 11. The computer program product of claim 10, wherein the deep learning engine outputs second identifiers associated with the synthesized payload, and wherein the second identifiers are associated with atomic elements that comprise the synthesized payload. 12. The computer program product of claim 10, wherein in response to generating the synthesized payload, the synthesized payload is used to detect a software vulnerability, thereby facilitating improved software security and improved processing time. 13. The computer program product of claim 9, wherein the valid result is determined based on one or more rules that define composition of clauses, phrases, and words in a language. 14. The computer program product of claim 9, wherein the valid result is determined based on executing, by the processing component, the payload in a browser-based component. 15. The computer program product of claim 9, wherein the payload is written in a dynamic programming language. 16. The computer program product of claim 9, wherein the one or more identifiers are supplied to the deep learning engine as a vector. 17. A device, comprising: a validation component of a payload generator that determines that a payload is syntactically correct and generates a valid result; and a mapping component of the payload generator that: atomizes the payload into a plurality of constituent atomic elements; maps the plurality of constituent atomic elements to a plurality of unique identifiers; and supplies the plurality of unique identifiers to a deep learning engine as a vector of unique identifiers. 18. The device of claim 17, wherein the mapping component atomizes the payload into the plurality of constituent atomic elements based on a grammar that defines a composition of clauses, phrases, and words in a language. 19. The device of claim 17, wherein the validation component determines that the payload is syntactically correct based on a grammar that defines a composition of clauses, phrases, and words in a language. 20. The device of claim 17, wherein the validation component determines that the payload generates a valid result based on executing the payload in a browser-based component.