AI Hacking
Specialized security assessments for artificial intelligence systems, identifying vulnerabilities in models, data, APIs, and governance to protect your AI solutions against advanced attacks.
Model Exploitation
Assessment of vulnerabilities in Machine Learning and Deep Learning models to identify and mitigate exploitation risks that could compromise the integrity, confidentiality, or availability of your AI systems.
Assessment of vulnerabilities in language models (LLMs) against malicious prompt injection attacks that can bypass restrictions or extract sensitive information.
Jailbreak testing to bypass ethical and security restrictions
Indirect injection attacks through user content
Robustness evaluation against prompt leaking techniques
Analysis of vulnerabilities in filtering and moderation systems
Analysis of the ability to recover training data or extract the complete model through interactions with the API or system.
Inversion attacks to recover sensitive training data
Model extraction testing through systematic queries
Evaluation of information leaks through embedding vectors
Confidentiality analysis in federated learning models
Generation of specially designed inputs to deceive the model and cause erroneous classifications or predictions with security implications.
Creation of adversarial examples for computer vision models
Evasion attacks in AI-based malware detection systems
Robustness testing against imperceptible perturbations
Evaluation of defenses such as adversarial training and input sanitization
Membership Inference Testing
Our assessments include membership inference tests to determine if it's possible to identify whether specific data was used in model training. This is especially critical for models trained with sensitive or confidential data, as it could represent a privacy violation or regulatory compliance issue.
Data Poisoning & Supply Chain Risks
Analysis of risks associated with training models with manipulated or malicious data, dataset integrity assessment, and security review of the AI supply chain, from development to deployment.
Assessment of the integrity and security of datasets used to train AI models, identifying possible manipulations or contaminations.
Analysis of vulnerabilities in data ingestion pipelines
Detection of malicious or manipulated data in the dataset
Robustness evaluation against poisoning attacks
Verification of data quality controls and validation
Security review in Machine Learning Operations (MLOps) environments, evaluating the security of CI/CD pipelines, infrastructure, and deployment processes.
Security analysis in training and deployment infrastructure
Evaluation of access controls and environment segregation
Security review in automated CI/CD pipelines
Verification of secure management of secrets and credentials
Assessment of risks associated with external dependencies such as libraries, frameworks, and third-party services used in the development and deployment of AI models.
Vulnerability analysis in dependencies and libraries
Risk assessment in ML/AI cloud services used
Integrity verification in artifacts and pre-trained models
Review of security controls in external data providers
Model Backdoor Attacks
We evaluate the resistance of your models against backdoor attacks, where an attacker could introduce malicious behaviors that are only activated under specific conditions. These attacks are particularly dangerous because the model functions correctly in most cases, making it difficult to detect the vulnerability until it is exploited.
Model & API Security
Review of exposed endpoints serving the model through REST or GraphQL APIs, evaluation of access controls, input validation, and protection against information leaks through user interaction.
Comprehensive assessment of endpoints exposing AI models through REST APIs, GraphQL, or other protocols, identifying vulnerabilities in the implementation.
Authentication and authorization analysis in AI endpoints
Input validation and sanitization evaluation
Rate limiting and abuse protection testing
Verification of secure configurations in servers and proxies
Review of access control mechanisms to ensure that only authorized users can interact with AI models and their capabilities.
Evaluation of granularity in permissions and roles
Analysis of access segregation between models and functionalities
Access control bypass testing
Verification of least privilege principle implementation
Identification of sensitive information leaks through model responses, metadata, or observable behaviors in user interaction.
Analysis of training data exposure in responses
Evaluation of information leaks in embedding vectors
Information extraction testing through elicitation techniques
Verification of sensitive information filtering in responses
Inference Security
We evaluate security during the inference phase, identifying vulnerabilities such as timing, side-channel, and oracle attacks that could allow an attacker to extract information about the model or its training data through patterns in response times or observable behaviors.
Abuse Monitoring
We analyze the effectiveness of monitoring and abuse detection systems in your AI APIs, evaluating the ability to identify patterns of malicious use, exploitation attempts, and coordinated attacks that could go unnoticed with traditional controls.
Governance, Bias & Compliance
Assessment of transparency, traceability, and regulatory compliance in AI systems, along with analysis of biases, unexplainable decisions, and potential ethical impacts that could affect specific users or groups.
Assessment of compliance with regulations and standards applicable to AI systems, such as the European AI Act, ethical guidelines, and internal policies.
Compliance analysis with the AI Act and other regulations
Evaluation of model documentation and transparency
Verification of accountability mechanisms
Review of usage policies and terms of service
Identification and evaluation of biases in AI models that could result in discrimination or unfair treatment of certain groups or individuals.
Analysis of biases in training data and results
Impact assessment on protected or minority groups
Algorithmic fairness and justice testing
Recommendations for mitigating identified biases
Assessment of the system's ability to explain its decisions and provide transparency about its internal functioning.
Analysis of implemented explainability mechanisms
Evaluation of algorithmic decision traceability
Verification of technical and non-technical documentation
Interpretability testing for end users
Ethical Impact Assessment
We conduct ethical impact assessments to identify and mitigate potential risks associated with the use of AI systems in different contexts. This includes analysis of social, economic, and environmental impacts, as well as recommendations for implementing safeguards that ensure responsible and ethical use of the technology.
Is your AI model secure?
Our AI security experts can help you identify and mitigate vulnerabilities in your models, data, and artificial intelligence systems before they are exploited by attackers.