penetration testers

Abstract: Existing jailbreak attackers fall into two classes: static planners fix their single- or multi-turn adversarial plans in advance, remaining simple but losing sight of the victim's replies, wasting turns and limiting transferability; interactive generators read each victim reply to generate the next-turn attack but require in-loop evaluators for success signals or hints, incurring substantial costs and inflated performance. Bridging this gap, we propose SEMA-Auto, a sighted and efficient multi-turn attacker with autonomy, interacting with the victim model and terminating the session by itself upon success. To stabilize its behaviors and boost its capabilities, SEMA-Auto is trained in two stages. Synthetic bootstrap tuning installs the notion of attack targets and conforming decisions by fine-tuning the base model with bootstrapped trajectories. Decision-supervised reinforcement learning then optimizes two objectives jointly yet separately, reinforcing the attack with higher jailbreak reward while supervising the decision at each turn against an absolute target. Our method achieves state-of-the-art (SOTA) attack success rates (ASR) across multiple datasets, victims, and judges, outperforming all static and interactive baselines, within almost the fewest turns, demonstrating both effectiveness and efficiency. For example, SEMA-Auto performs a $76.0\%$ ASR@1 within an average of $3.0$ turns against DeepSeek-V4-Flash on AdvBench, while the best static baseline is $54.8\%$ with fixed $7$ turns and the best interactive baseline is $43.8\%$ in $3.7$ turns. Against safety-hardened frontier models, GPT-oss-120B, GPT-5.4, Claude-Haiku-4.5, and GPT-6-Astra, our counter-trained attacker achieves an average $59.6\%$ ASR@1 on HarmBench, $13.8\%$ over prior SOTA. Our code is available at: https://github.com/microsoft/SEMA

Abstract: How can an AI agent automatically discover security vulnerabilities in any software repository? Existing approaches use LLMs to generate software vulnerability exploits. However, it is challenging for an LLM to reliably verify its own generated exploits. In this paper, we propose Ulysses, a multi-agent security harness to synthesize verifiable vulnerability exploits for web applications. Ulysses converts candidate vulnerability findings into verified Proofs of Exploitability by iteratively exploring exploit hypotheses and establishing success in independently observed execution effects. We evaluated Ulysses on WebExploitGym, a curated benchmark of exploitable instances from 25 real-world web applications that span 5 languages and 7 frameworks. Ulysses produces verified proofs for 10 of 25 real-world advisories. It also discovered 4 new proofs of exploitability that have never been previously reported.

Abstract: The rapid advancement of cybersecurity agents in vulnerability discovery has increased the need for benchmarks that evaluate their capabilities accurately. However, synthetic benchmarks lack realism, real-life benchmarks are prone to contamination, and many existing benchmarks have already been saturated by current models. We therefore set out to develop a benchmark that is realistic, reproducible, resistant to contamination, and challenging. Our key insight is that real-life business-logic vulnerabilities are subtle and exercise complex application interactions, making them naturally demanding. The difficulty lies in specifying evaluation criteria precise enough to faithfully measure model capabilities while keeping the tasks free of data-leakage. We introduce AnonVul, a cross-language benchmark of 132 application-level business-logic vulnerabilities, built by a semi-automated pipeline that anonymizes each task, injects oracles from expert-written specifications, and differentially verifies proof-of-concept exploits. The benchmark spans nine languages, with two evaluation tracks on black-box and grey-box settings, where each task runs in a sandbox with specialized oracles embedded in key exploitation steps. We evaluate six state-of-the-art vulnerability-discovery agents on the AnonVul benchmark, measuring discovery success, cost-effectiveness, anonymization sensitivity, oracle validity, and failure modes. Our results show that AnonVul remains challenging, with the best agent succeeding in only 23.11% of attempts. On a matched cohort of 20 vulnerabilities, restoring software identity increases discovery from 21.25% to 40.83%, highlighting the importance of controlling recognizable software knowledge when evaluating vulnerability discovery.

Abstract: Passwords remain the dominant online authentication mechanism, and understanding how humans choose them is essential for defensive strength estimation and attack simulation alike. Recent learning-based approaches such as PassGAN and PassGPT have shown that deep generative models can learn password structure directly from leaked corpora. However, both train from random initialization on password data alone. The role of linguistic prior knowledge in password modeling, and what it reveals about how humans create secrets, remains largely underexplored. Here, we address this gap with PassGPT+, which adapts the linguistic prior of GPT-2 to password observations through character-aware tokenization. We also introduce PassDiffusion, the first absorbing-state discrete diffusion model for password generation, as a probe of whether non-autoregressive approaches are competitive. On the RockYou benchmark, PassGPT+ recovers 22.53% of held-out passwords at 108 guesses, a 16% relative gain over PassGPT, and retains 79% of this match rate when transferred without retraining to a disjoint 2020 leak dataset, demonstrating that linguistic priors capture persistent regularities of human password generation. PassDiffusion underperforms by two to three orders of magnitude, indicating that autoregressive modeling is substantially better matched than iterative denoising to the discrete, exact-match nature of password generation.

Abstract: Growing vulnerability disclosure and widespread software reuse increase security teams' need for reproducible evidence to diagnose vulnerabilities, validate patches, and build regression tests. Producing such evidence at scale requires automated end-to-end CVE reproduction. Existing methods typically process different CVEs through a uniform pipeline, but differences in runtime form, trigger interfaces, and prerequisite state impose different execution requirements on individual stages, making fixed workflows difficult to adapt to diverse reproduction needs. To address this problem, we present TAILOR, a type- and state-aware multi-agent framework specialized for complex vulnerability reproduction. TAILOR converts static vulnerability information into auditable reproduction evidence and packages reconstructed environments and trigger evidence into reproduction artifacts. Its first-level type-aware mechanism adaptively matches each vulnerability to an execution path. Within the Web path, its second-level state-aware mechanism constructs the required prerequisite state before exploitation, decouples prerequisite-state construction from core vulnerability triggering, and shares execution constraints across exploitation and verification. We construct a dataset of 200 CVEs with an emphasis on cases with complex execution requirements. TAILOR successfully reproduces 59.24\% of Web vulnerabilities and 44.19\% of traditional vulnerabilities. Further ablation experiments show that the two control levels respectively mitigate execution-path mismatch and missing Web prerequisite state. Overall, TAILOR broadens the coverage of automated CVE reproduction and provides auditable evidence for vulnerability diagnosis and defense.